{
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  "metadata": {
    "colab": {
      "name": "GREMLIN_TF_v2_BETA.ipynb",
      "version": "0.3.2",
      "provenance": [],
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      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sokrypton/GREMLIN_CPP/blob/master/GREMLIN_TF_v2_BETA.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mu2S86VhS-8t",
        "colab_type": "text"
      },
      "source": [
        "# GREMLIN_TF v2.1.BETA.1\n",
        "GREMLIN implemented in tensorflow\n",
        "\n",
        "### Change log:\n",
        "*   19Apr2019\n",
        " - added option to ignore gaps (now the default)\n",
        " - added option to initialize v and w from input\n",
        " - added function to score sequences (for design, ranking-mutations/homologys)\n",
        "*   02Apr2019\n",
        " - fixed a few hard-coded values, to allow GREMLIN to work with any alphabet (binary, protein, rna etc)\n",
        "*   22Jan2019\n",
        " - moving [GREMLIN_TF_simple](https://colab.research.google.com/github/sokrypton/GREMLIN_CPP/blob/master/GREMLIN_TF_simple.ipynb) to a seperate notebook\n",
        "*   19Jan2019\n",
        " - in the past we found that optimizing V first, required less iterations for convergence. Since V can be computed exactly (assuming no W), we replace this first optimization step with a simple V initialization.\n",
        " - a few variables were renamed to be consistent with the c++ version\n",
        "*   16Jan2019\n",
        " - updated how indices are handled (for easier/cleaner parsing)\n",
        " - minor speed up in how we symmetrize and zero the diagional of W\n",
        "*   15Jan2019\n",
        " - LBFGS optimizer replaced with a modified version of the ADAM optimizer\n",
        " - Added option for stochastic gradient descent (via batch_size)\n",
        "  \n",
        "### Method:\n",
        "GREMLIN takes a multiple sequence alignment (MSA) and returns a Markov Random Field (MRF). The MRF consists of a one-body term (V) that encodes conservation, and a two-body term (W) that encodes co-evolution.\n",
        "\n",
        "For more details about the method see:\n",
        "[Google slides](https://docs.google.com/presentation/d/1aooxoksosSv7CWs9-ktqhUjyXR3wrgbG5a6PCr92od4/) and accompanying [Google colab](https://colab.research.google.com/drive/17RJcExuyifnd7ShTcsZGh6mBpWq0-s60)\n",
        "\n",
        "See [GREMLIN_TF_simple](https://colab.research.google.com/github/sokrypton/GREMLIN_CPP/blob/master/GREMLIN_TF_simple.ipynb) for a stripped down version of this code (with no funky gap removal, sequence weight, etc). This is intented for educational purpose,  and could also be very useful for anyone trying to modify or improve the algorithm!\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yM3wyYU5SwYn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ------------------------------------------------------------------\n",
        "# \"THE BEERWARE LICENSE\" (Revision 42)\n",
        "# ------------------------------------------------------------------\n",
        "# <so@g.harvard.edu> and <pkk382@g.harvard.edu> wrote this code.\n",
        "# As long as you retain this notice, you can do whatever you want\n",
        "# with this stuff. If we meet someday, and you think this stuff\n",
        "# is worth it, you can buy us a beer in return.\n",
        "# --Sergey Ovchinnikov and Peter Koo\n",
        "# ------------------------------------------------------------------\n",
        "# The original MATLAB code for GREMLIN was written by Hetu Kamisetty\n",
        "# ------------------------------------------------------------------"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NLUvPVyxb7bo",
        "colab_type": "text"
      },
      "source": [
        "## External libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YyJpLM_tJfrY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# IMPORTANT, only tested using PYTHON 3!\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pylab as plt\n",
        "from scipy import stats\n",
        "from scipy.spatial.distance import pdist, squareform\n",
        "from scipy.special import logsumexp\n",
        "import pandas as pd"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j0Yp7bPRmvwU",
        "colab_type": "text"
      },
      "source": [
        "## Global parameters"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o3c7KURqmugY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "################\n",
        "# note: if you are modifying the alphabet\n",
        "# make sure last character is \"-\" (gap)\n",
        "################\n",
        "alphabet = \"ARNDCQEGHILKMFPSTWYV-\"\n",
        "states = len(alphabet)\n",
        "\n",
        "# map amino acids to integers (A->0, R->1, etc)\n",
        "a2n = dict((a,n) for n,a in enumerate(alphabet))\n",
        "aa2int = lambda x: a2n.get(x,a2n['-'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bX6GXKV3I2pm",
        "colab_type": "text"
      },
      "source": [
        "## Parse MSA (Multiple Seq Alignment)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zA0Bne59SUIu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# from fasta\n",
        "def parse_fasta(filename):\n",
        "  '''function to parse fasta'''\n",
        "  header = []\n",
        "  sequence = []\n",
        "  lines = open(filename, \"r\")\n",
        "  for line in lines:\n",
        "    line = line.rstrip()\n",
        "    if line[0] == \">\":\n",
        "      header.append(line[1:])\n",
        "      sequence.append([])\n",
        "    else:\n",
        "      sequence[-1].append(line)\n",
        "  lines.close()\n",
        "  sequence = [''.join(seq) for seq in sequence]\n",
        "  return np.array(header), np.array(sequence)\n",
        "\n",
        "def filt_gaps(msa, gap_cutoff=0.5):\n",
        "  '''filters alignment to remove gappy positions'''\n",
        "  frac_gaps = np.mean((msa == states-1).astype(np.float),0)\n",
        "  non_gaps = np.where(frac_gaps < gap_cutoff)[0]\n",
        "  return msa[:,non_gaps], non_gaps\n",
        "\n",
        "def get_eff(msa, eff_cutoff=0.8):\n",
        "  '''compute effective weight for each sequence'''  \n",
        "  msa_sm = 1.0 - squareform(pdist(msa,\"hamming\"))\n",
        "  msa_w = (msa_sm >= eff_cutoff).astype(np.float)\n",
        "  msa_w = 1.0/np.sum(msa_w,-1)\n",
        "  return msa_w\n",
        "\n",
        "def str2int(x):\n",
        "  '''convert a list of strings into list of integers'''\n",
        "  # Example: [\"ACD\",\"EFG\"] -> [[0,4,3], [6,13,7]]\n",
        "  if x.dtype.type is np.str_:\n",
        "    if x.ndim == 0: return np.array([aa2int(aa) for aa in x])\n",
        "    else: return np.array([[aa2int(aa) for aa in seq] for seq in x])\n",
        "  else:\n",
        "    return x\n",
        "  \n",
        "def split_train_test(seqs, frac_test=0.1):\n",
        "  # shuffle data\n",
        "  x = np.copy(seqs)\n",
        "  np.random.shuffle(x[1:])\n",
        "\n",
        "  # fraction of data used for testing\n",
        "  split = int(len(x) * (1.0-frac_test))\n",
        "\n",
        "  # split training/test datasets\n",
        "  return x[:split], x[split:]\n",
        "\n",
        "def mk_msa(seqs, gap_cutoff=0.5, eff_cutoff=0.8):\n",
        "  '''converts list of sequences to MSA (Multiple Sequence Alignment)'''\n",
        "  # =============================================================================\n",
        "  # The function takes a list of sequences (strings) and returns a (dict)ionary\n",
        "  # containing the following:\n",
        "  # =============================================================================\n",
        "  # BEFORE GAP REMOVAL\n",
        "  # -----------------------------------------------------------------------------\n",
        "  # msa_ori   msa\n",
        "  # ncol_ori  number of columns\n",
        "  # -----------------------------------------------------------------------------\n",
        "  # AFTER GAP REMOVAL\n",
        "  # By default, columns with ≥ 50% gaps are removed. This makes things a\n",
        "  # little complicated, as we need to keep track of which positions were removed.\n",
        "  # -----------------------------------------------------------------------------\n",
        "  # msa       msa\n",
        "  # ncol      number of columns\n",
        "  # v_idx     index of positions kept\n",
        "  # -----------------------------------------------------------------------------\n",
        "  # weights   weight for each sequence (based on sequence identity)\n",
        "  # nrow      number of rows (sequences)\n",
        "  # neff      number of effective sequences sum(weights)\n",
        "  # =============================================================================\n",
        "  \n",
        "  msa_ori = str2int(seqs)\n",
        "\n",
        "  # remove positions with more than > 50% gaps\n",
        "  msa, v_idx = filt_gaps(msa_ori)\n",
        "  \n",
        "  # compute effective weight for each sequence\n",
        "  msa_weights = get_eff(msa, eff_cutoff)\n",
        "    \n",
        "  return {\"msa_ori\":msa_ori,\n",
        "          \"msa\":msa,\n",
        "          \"weights\":msa_weights,\n",
        "          \"neff\":np.sum(msa_weights),\n",
        "          \"v_idx\":v_idx,\n",
        "          \"nrow\":msa.shape[0],\n",
        "          \"ncol\":msa.shape[1],\n",
        "          \"ncol_ori\":msa_ori.shape[1]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fky6gk-HlFyi",
        "colab_type": "text"
      },
      "source": [
        "## GREMLIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gx14M7Tvu-Ct",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# optimizer\n",
        "def opt_adam(loss, name, var_list=None, lr=1.0, b1=0.9, b2=0.999, b_fix=False):\n",
        "  # adam optimizer\n",
        "  # Note: this is a modified version of adam optimizer. More specifically, we replace \"vt\"\n",
        "  # with sum(g*g) instead of (g*g). Furthmore, we find that disabling the bias correction\n",
        "  # (b_fix=False) speeds up convergence for our case.\n",
        "  \n",
        "  if var_list is None: var_list = tf.trainable_variables() \n",
        "  gradients = tf.gradients(loss,var_list)\n",
        "  if b_fix: t = tf.Variable(0.0,\"t\")\n",
        "  opt = []\n",
        "  for n,(x,g) in enumerate(zip(var_list,gradients)):\n",
        "    if g is not None:\n",
        "      ini = dict(initializer=tf.zeros_initializer,trainable=False)\n",
        "      mt = tf.get_variable(name+\"_mt_\"+str(n),shape=list(x.shape), **ini)\n",
        "      vt = tf.get_variable(name+\"_vt_\"+str(n),shape=[], **ini)\n",
        "      \n",
        "      mt_tmp = b1*mt+(1-b1)*g\n",
        "      vt_tmp = b2*vt+(1-b2)*tf.reduce_sum(tf.square(g))\n",
        "      lr_tmp = lr/(tf.sqrt(vt_tmp) + 1e-8)\n",
        "\n",
        "      if b_fix: lr_tmp = lr_tmp * tf.sqrt(1-tf.pow(b2,t))/(1-tf.pow(b1,t))\n",
        "\n",
        "      opt.append(x.assign_add(-lr_tmp * mt_tmp))\n",
        "      opt.append(vt.assign(vt_tmp))\n",
        "      opt.append(mt.assign(mt_tmp))\n",
        "        \n",
        "  if b_fix: opt.append(t.assign_add(1.0))\n",
        "  return(tf.group(opt))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqYqlJAXVI9N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def GREMLIN(msa,\n",
        "            opt_iter=100,\n",
        "            opt_rate=1.0,\n",
        "            batch_size=None,\n",
        "            lam_v=0.01,\n",
        "            lam_w=0.01,\n",
        "            scale_lam_w=True,\n",
        "            v=None,\n",
        "            w=None,\n",
        "            ignore_gap=True):\n",
        "  \n",
        "  '''fit params of MRF (Markov Random Field) given MSA (multiple sequence alignment)'''\n",
        "  # ==========================================================================\n",
        "  # this function takes a MSA (dict)ionary, from mk_msa() and returns a MRF\n",
        "  # (dict)ionary containing the following:\n",
        "  # ==========================================================================\n",
        "  # len       full length\n",
        "  # v_idx     index of positions (mapping back to full length)\n",
        "  # v         2-body term\n",
        "  # w         2-body term\n",
        "  # ==========================================================================\n",
        "  # WARNING: The mrf is over the msa after gap removal. \"v_idx\" and \"len\" are\n",
        "  # important for mapping the MRF back to the original MSA.\n",
        "  # ==========================================================================\n",
        "  \n",
        "  ########################################\n",
        "  # SETUP COMPUTE GRAPH\n",
        "  ########################################\n",
        "  # reset tensorflow graph\n",
        "  tf.reset_default_graph()\n",
        "  \n",
        "  # length of sequence\n",
        "  ncol = msa[\"ncol\"] \n",
        "  \n",
        "  # input msa (multiple sequence alignment) \n",
        "  MSA = tf.placeholder(tf.int32,shape=(None,ncol),name=\"msa\")\n",
        "  \n",
        "  # input msa weights\n",
        "  MSA_weights = tf.placeholder(tf.float32, shape=(None,), name=\"msa_weights\")\n",
        "  \n",
        "  # one-hot encode msa\n",
        "  OH_MSA = tf.one_hot(MSA,states)\n",
        "  \n",
        "  if ignore_gap:\n",
        "    ncat = states - 1\n",
        "    NO_GAP = 1.0 - OH_MSA[...,-1] \n",
        "    OH_MSA = OH_MSA[...,:ncat]\n",
        "    \n",
        "  else:\n",
        "    ncat = states\n",
        "  \n",
        "  ########################################\n",
        "  # V: 1-body-term of the MRF\n",
        "  ########################################\n",
        "  V = tf.get_variable(name=\"V\",\n",
        "                          shape=[ncol,ncat],\n",
        "                          initializer=tf.zeros_initializer)\n",
        "  \n",
        "  ########################################\n",
        "  # W: 2-body-term of the MRF\n",
        "  ########################################\n",
        "  W_tmp = tf.get_variable(name=\"W\",\n",
        "                          shape=[ncol,ncat,ncol,ncat],\n",
        "                          initializer=tf.zeros_initializer)  \n",
        "  \n",
        "  # symmetrize W\n",
        "  W = W_tmp + tf.transpose(W_tmp,[2,3,0,1])\n",
        "  \n",
        "  # set diagonal to zero\n",
        "  W = W * (1-np.eye(ncol))[:,None,:,None]\n",
        "\n",
        "  ########################################\n",
        "  # Pseudo-Log-Likelihood\n",
        "  ########################################\n",
        "  # V + W\n",
        "  VW = V + tf.tensordot(OH_MSA,W,2)\n",
        "  \n",
        "  # hamiltonian\n",
        "  H = tf.reduce_sum(OH_MSA*VW,-1)\n",
        "  \n",
        "  # local Z (parition function)\n",
        "  Z = tf.reduce_logsumexp(VW,-1)\n",
        "\n",
        "  PLL = H - Z\n",
        "  if ignore_gap:\n",
        "    PLL = PLL * NO_GAP  \n",
        "\n",
        "  PLL = tf.reduce_sum(PLL,-1)  \n",
        "  PLL = tf.reduce_sum(MSA_weights * PLL)/tf.reduce_sum(MSA_weights)\n",
        "\n",
        "  ########################################\n",
        "  # Regularization\n",
        "  ########################################\n",
        "  L2 = lambda x: tf.reduce_sum(tf.square(x))\n",
        "  L2_V = lam_v * L2(V)\n",
        "  L2_W = lam_w * L2(W) * 0.5\n",
        "  \n",
        "  if scale_lam_w:\n",
        "    L2_W = L2_W * (ncol-1) * (states-1)\n",
        "  \n",
        "  ########################################\n",
        "  # Loss Function\n",
        "  ########################################\n",
        "  # loss function to minimize\n",
        "  loss = -PLL + (L2_V + L2_W) / msa[\"neff\"]\n",
        "  \n",
        "  # optimizer\n",
        "  opt = opt_adam(loss,\"adam\",lr=opt_rate)\n",
        "  \n",
        "  ########################################\n",
        "  # Input Generator\n",
        "  ########################################\n",
        "  all_idx = np.arange(msa[\"nrow\"])\n",
        "  def feed(feed_all=False):\n",
        "    if batch_size is None or feed_all:\n",
        "      return {MSA:msa[\"msa\"], MSA_weights:msa[\"weights\"]}\n",
        "    else:\n",
        "      batch_idx = np.random.choice(all_idx,size=batch_size)\n",
        "      return {MSA:msa[\"msa\"][batch_idx], MSA_weights:msa[\"weights\"][batch_idx]}\n",
        "  \n",
        "  ########################################\n",
        "  # OPTIMIZE\n",
        "  ########################################\n",
        "  with tf.Session() as sess:\n",
        "    \n",
        "    # initialize variables V and W\n",
        "    sess.run(tf.global_variables_initializer())\n",
        "\n",
        "    # initialize V\n",
        "    if v is None:\n",
        "      oh_msa = np.eye(states)[msa[\"msa\"]]\n",
        "      if ignore_gap: oh_msa = oh_msa[...,:-1]\n",
        "      \n",
        "      pseudo_count = 0.01 * np.log(msa[\"neff\"])\n",
        "      f_v = np.einsum(\"nla,n->la\",oh_msa,msa[\"weights\"])\n",
        "      V_ini = np.log(f_v + pseudo_count)\n",
        "      if lam_v > 0:\n",
        "        V_ini = V_ini - np.mean(V_ini,axis=-1,keepdims=True)\n",
        "      sess.run(V.assign(V_ini))\n",
        "      \n",
        "    else:\n",
        "      sess.run(V.assign(v))\n",
        "\n",
        "    # initialize W\n",
        "    if w is not None:\n",
        "      sess.run(W_tmp.assign(w * 0.5))\n",
        "      \n",
        "    # compute loss across all data\n",
        "    get_loss = lambda: np.round(sess.run(loss,feed(True)) * msa[\"neff\"],2)\n",
        "\n",
        "    print(\"starting\",get_loss())      \n",
        "    for i in range(opt_iter):\n",
        "      sess.run(opt,feed())  \n",
        "      if (i+1) % int(opt_iter/10) == 0:\n",
        "        print(\"iter\",(i+1),get_loss())\n",
        "    \n",
        "    # save the V and W parameters of the MRF\n",
        "    V_ = sess.run(V)\n",
        "    W_ = sess.run(W)\n",
        "    \n",
        "  ########################################\n",
        "  # return MRF\n",
        "  ########################################\n",
        "  no_gap_states = states - 1\n",
        "  mrf = {\"v\": V_[:,:no_gap_states],\n",
        "         \"w\": W_[:,:no_gap_states,:,:no_gap_states],\n",
        "         \"v_idx\": msa[\"v_idx\"],\n",
        "         \"len\": msa[\"ncol_ori\"]}\n",
        "  \n",
        "  return mrf"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mppg0JLtP25z",
        "colab_type": "text"
      },
      "source": [
        "## EXAMPLE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnigmLmAlyWv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# download example fasta MSA\n",
        "!wget -q -nc https://gremlin2.bakerlab.org/db/PDB_EXP/fasta/4FAZA.fas"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osaZwTSMOicF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ==========================================================================\n",
        "# PREP MSA\n",
        "# ==========================================================================\n",
        "# parse fasta\n",
        "headers, seqs = parse_fasta(\"4FAZA.fas\")\n",
        "\n",
        "train_seqs, test_seqs = split_train_test(seqs, frac_test=0.1)\n",
        "\n",
        "# process input training sequences\n",
        "msa = mk_msa(train_seqs, gap_cutoff=0.5, eff_cutoff=0.8)\n",
        "# gap_cutoff=0.5 (positions with ≥ 50% gaps are removed)\n",
        "# eff_cutoff=0.8 (sequences that share ≥ 80% sequence identity \n",
        "# are considered \"effectively\" a single sequence)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CoBRuqmVVrbD",
        "colab_type": "code",
        "outputId": "aa6f6dee-b239-43f6-8bb6-d4d135d2bbe5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "%%time\n",
        "# ==========================================================================\n",
        "# RUN GREMLIN\n",
        "# ==========================================================================\n",
        "mrf = GREMLIN(msa,lam_w=0.1)\n",
        "\n",
        "# NOTE: lam_v (for one-body term) lam_w (for two-body term) can be used to regularize the model\n",
        "#\n",
        "# for contact prediction we find lam_w = 0.01 to be most optimial\n",
        "# (even though it's technically overfitting on the data!)\n",
        "# the overfitting is partly corrected by APC\n",
        "#\n",
        "# for design/scoring you may want to bump the lam_w to a higher value!\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "starting 132384.42\n",
            "iter 10 46548.59\n",
            "iter 20 37993.85\n",
            "iter 30 35229.25\n",
            "iter 40 34122.76\n",
            "iter 50 33641.43\n",
            "iter 60 33420.88\n",
            "iter 70 33300.3\n",
            "iter 80 33223.99\n",
            "iter 90 33168.34\n",
            "iter 100 33123.34\n",
            "CPU times: user 965 ms, sys: 138 ms, total: 1.1 s\n",
            "Wall time: 1.06 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kPL-5atH4iVZ",
        "colab_type": "text"
      },
      "source": [
        "## Use the MRF model to score \"new\" sequences\n",
        "The source of \"new\" sequences maybe:\n",
        " - Test sequences (to check for overfitting)\n",
        " - Mutant sequences (to predict effects of mutations)\n",
        " - Newly sequenced genomes (to test fit to model, for annotation and homology search)\n",
        " \n",
        "Note, high score means better fit to model. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ANAjrL5E-k-Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "###############\n",
        "## FUNCTIONS\n",
        "###############\n",
        "def score(mrf, x, recompute_z=False):\n",
        "  x = str2int(x)\n",
        "\n",
        "  # if length of sequence != length of model\n",
        "  if x.shape[-1] != len(mrf[\"v_idx\"]):\n",
        "    x = x[...,mrf[\"v_idx\"]]\n",
        "  \n",
        "  # one hot encode\n",
        "  x = np.eye(states)[x] \n",
        "  \n",
        "  # get non-gap positions\n",
        "  no_gap = 1.0 - x[...,-1]\n",
        "  \n",
        "  # remove gap from one-hot-encoding\n",
        "  x = x[...,:-1]\n",
        "  \n",
        "  # compute score\n",
        "  vw = mrf[\"v\"] + np.tensordot(x,mrf[\"w\"],2)\n",
        "  \n",
        "  # ============================================================================================\n",
        "  # Note, Z (the partition function) is a constant. In GREMLIN, V, W & Z are estimated using all\n",
        "  # the original weighted input sequence(s). It is NOT recommended to recalculate Z with a \n",
        "  # different set of sequences. Given the common ERROR of recomputing Z, we include the option \n",
        "  # to do so, for comparison.\n",
        "  # ============================================================================================\n",
        "  h = np.sum(np.multiply(x,vw),axis=-1)\n",
        "  if recompute_z:\n",
        "    z = logsumexp(vw, axis=-1)\n",
        "    return np.sum((h-z), axis=-1)\n",
        "  else:\n",
        "    return np.sum(h, axis=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8hhfv2I9Jc9",
        "colab_type": "code",
        "outputId": "b7a2ecde-43d8-44d4-b092-eb34583bb139",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "# provide a single sequence\n",
        "print(seqs[0])\n",
        "score(mrf,seqs[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "PFAQIYLIEGRTEEQKRAVIEKVTQAMMEAVGAPKENVRVWIHDVPKENWGIGGVSAKALGR\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "339.599715080054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aU2hoUY9eQF",
        "colab_type": "code",
        "outputId": "55701155-85c3-4678-8573-adf41e122704",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# provide multiple sequences\n",
        "print(seqs[0:3])\n",
        "score(mrf,seqs[0:3])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['PFAQIYLIEGRTEEQKRAVIEKVTQAMMEAVGAPKENVRVWIHDVPKENWGIGGVSAKALGR'\n",
            " 'PIVQVVLIAGRTDEQKTRLIAGLTDSVVTVLGVGAESVRVFIKDIPNTEFGIGGATAASLGR'\n",
            " 'SIIQVFFIAGRTDEQKERLIGALTDAAVKTIGIDRSDVRVILKDIPNTEYGIAGKTAKSLGR']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([339.59971508, 279.65242076, 287.80652799])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZOM09Ut9nSE",
        "colab_type": "code",
        "outputId": "82086485-cca8-4b93-decd-c01c844b6b04",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        }
      },
      "source": [
        "# provide all the sequences\n",
        "train_scos = score(mrf, train_seqs)\n",
        "test_scos = score(mrf, test_seqs)\n",
        "plt.hist(train_scos,bins=10,alpha=0.5,density=True,label=\"train\")\n",
        "plt.hist(test_scos,bins=10,alpha=0.5,density=True,label=\"test\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCrfC2Um4xww",
        "colab_type": "text"
      },
      "source": [
        "## Explore the contact map\n",
        "### Contact prediction:\n",
        "\n",
        "For contact prediction, the W matrix is reduced from LxLx20x20 to LxL matrix (by taking the L2norm for each of the 20x20). In the code below, you can access this as mtx[\"raw\"]. Further correction (average product correction) is then performed to the mtx[\"raw\"] to remove the effects of entropy, mtx[\"apc\"]. The relative ranking of mtx[\"apc\"] is used to assess importance. When there are enough effective sequences (>1000), we find that the top 1.0L contacts are ~90% accurate! When the number of effective sequences is lower, NN can help clean noise and fill in missing contacts.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nMxp7up_P1_q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "###############\n",
        "## FUNCTIONS\n",
        "###############\n",
        "def normalize(x):\n",
        "  x = stats.boxcox(x - np.amin(x) + 1.0)[0]\n",
        "  x_mean = np.mean(x)\n",
        "  x_std = np.std(x)\n",
        "  return((x-x_mean)/x_std)\n",
        "\n",
        "def get_mtx(mrf):\n",
        "  '''convert MRF (Markov Random Field) to MTX (Matrix or Contact-map)'''\n",
        "  \n",
        "  # raw (l2norm of each 20x20 matrix)\n",
        "  raw_sq = np.sqrt(np.sum(np.square(mrf[\"w\"]),(1,3)))\n",
        "  raw = squareform(raw_sq, checks=False)\n",
        "\n",
        "  # apc (average product correction)\n",
        "  ap_sq = np.sum(raw_sq,0,keepdims=True) * np.sum(raw_sq,1,keepdims=True)/np.sum(raw_sq)\n",
        "  apc = squareform(raw_sq - ap_sq, checks=False)\n",
        "\n",
        "  i, j = np.triu_indices_from(raw_sq,k=1)\n",
        "  mtx = {\n",
        "         \"i\": mrf[\"v_idx\"][i],\n",
        "         \"j\": mrf[\"v_idx\"][j],\n",
        "         \"raw\": raw,\n",
        "         \"apc\": apc,\n",
        "         \"zscore\": normalize(apc),\n",
        "         \"len\": mrf[\"len\"]\n",
        "  }  \n",
        "  return mtx\n",
        "\n",
        "def plot_mtx(mtx):\n",
        "  '''plot the mtx'''\n",
        "  plt.figure(figsize=(15,5))\n",
        "  for n, key in enumerate([\"raw\",\"apc\",\"zscore\"]):\n",
        "    \n",
        "    # create empty mtx\n",
        "    m = np.ones((mtx[\"len\"],mtx[\"len\"])) * np.nan\n",
        "    \n",
        "    # populate\n",
        "    m[mtx[\"i\"],mtx[\"j\"]] = mtx[key]\n",
        "    m[mtx[\"j\"],mtx[\"i\"]] = m[mtx[\"i\"],mtx[\"j\"]]\n",
        "    \n",
        "    #plot\n",
        "    plt.subplot(1,3,n+1)\n",
        "    plt.title(key)\n",
        "    if key == \"zscore\": plt.imshow(m, cmap='Blues', vmin=1, vmax=3)\n",
        "    else: plt.imshow(m, cmap='Blues')\n",
        "    plt.grid(False)\n",
        "    \n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RSleviAVPJ36",
        "colab_type": "code",
        "outputId": "1ebd9f9c-2d41-4291-f33a-0563f3bd1136",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "mtx = get_mtx(mrf)  \n",
        "plot_mtx(mtx)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DqH1Pfnlcz/efz3kuHK+TxvAdoLlmJ4050U4aw04aM9l+4XidtHdq8N4NiwqSjrPl2FE4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b2ctLMHrrfsDEeDnbTNlMO2mpy3U343BdWrbQ/g74746BPboM05aeykcffv+P5r0P7vD66y8aKf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\nq1HL8QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWaTHLTH5rGw",
        "colab_type": "text"
      },
      "source": [
        "## Look at top co-evolving residue pairs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9A2yeOJ8uPNM",
        "colab_type": "code",
        "outputId": "54629bf1-60ff-4bdf-9a9a-dd34fe09fde9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "######################################################################################\n",
        "# WARNING - WARNING - WARNING\n",
        "######################################################################################\n",
        "# - the index starts at 0\n",
        "# - the \"first\" position is 0\n",
        "# - in bioinformatics, the first position of a sequence is often \"1\"\n",
        "#   for this index use i_aa and j_aa!\n",
        "\n",
        "# adding amino acid to index+1\n",
        "seq = seqs[0]\n",
        "mtx[\"i_aa\"] = [f\"{seq[i]}_{i+1}\" for i in mtx[\"i\"]]\n",
        "mtx[\"j_aa\"] = [f\"{seq[j]}_{j+1}\" for j in mtx[\"j\"]]\n",
        "\n",
        "# load mtx into pandas dataframe\n",
        "pd_mtx = pd.DataFrame(mtx,columns=[\"i\",\"j\",\"raw\",\"apc\",\"zscore\",\"i_aa\",\"j_aa\"])\n",
        "\n",
        "# get contacts with sequence seperation > 5, sort\n",
        "top = pd_mtx.loc[pd_mtx['j'] - pd_mtx['i'] > 5].sort_values(\"apc\",ascending=False)\n",
        "\n",
        "# show top 10\n",
        "top.head(10)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>i</th>\n",
              "      <th>j</th>\n",
              "      <th>raw</th>\n",
              "      <th>apc</th>\n",
              "      <th>zscore</th>\n",
              "      <th>i_aa</th>\n",
              "      <th>j_aa</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>4</td>\n",
              "      <td>22</td>\n",
              "      <td>0.363484</td>\n",
              "      <td>0.264401</td>\n",
              "      <td>2.920720</td>\n",
              "      <td>I_5</td>\n",
              "      <td>V_23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1056</th>\n",
              "      <td>21</td>\n",
              "      <td>28</td>\n",
              "      <td>0.404643</td>\n",
              "      <td>0.252682</td>\n",
              "      <td>2.903955</td>\n",
              "      <td>K_22</td>\n",
              "      <td>E_29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>820</th>\n",
              "      <td>15</td>\n",
              "      <td>41</td>\n",
              "      <td>0.368699</td>\n",
              "      <td>0.230041</td>\n",
              "      <td>2.864381</td>\n",
              "      <td>K_16</td>\n",
              "      <td>I_42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>881</th>\n",
              "      <td>16</td>\n",
              "      <td>58</td>\n",
              "      <td>0.360851</td>\n",
              "      <td>0.221764</td>\n",
              "      <td>2.847098</td>\n",
              "      <td>R_17</td>\n",
              "      <td>A_59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>2</td>\n",
              "      <td>26</td>\n",
              "      <td>0.363327</td>\n",
              "      <td>0.207895</td>\n",
              "      <td>2.814072</td>\n",
              "      <td>A_3</td>\n",
              "      <td>M_27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>19</td>\n",
              "      <td>50</td>\n",
              "      <td>0.338385</td>\n",
              "      <td>0.197754</td>\n",
              "      <td>2.786229</td>\n",
              "      <td>I_20</td>\n",
              "      <td>G_51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1278</th>\n",
              "      <td>27</td>\n",
              "      <td>37</td>\n",
              "      <td>0.284180</td>\n",
              "      <td>0.188942</td>\n",
              "      <td>2.759123</td>\n",
              "      <td>M_28</td>\n",
              "      <td>V_38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>488</th>\n",
              "      <td>8</td>\n",
              "      <td>45</td>\n",
              "      <td>0.289913</td>\n",
              "      <td>0.156609</td>\n",
              "      <td>2.630593</td>\n",
              "      <td>E_9</td>\n",
              "      <td>P_46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>990</th>\n",
              "      <td>19</td>\n",
              "      <td>41</td>\n",
              "      <td>0.294071</td>\n",
              "      <td>0.143232</td>\n",
              "      <td>2.560453</td>\n",
              "      <td>I_20</td>\n",
              "      <td>I_42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1156</th>\n",
              "      <td>23</td>\n",
              "      <td>53</td>\n",
              "      <td>0.197730</td>\n",
              "      <td>0.140188</td>\n",
              "      <td>2.542782</td>\n",
              "      <td>T_24</td>\n",
              "      <td>G_54</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       i   j       raw       apc    zscore  i_aa  j_aa\n",
              "251    4  22  0.363484  0.264401  2.920720   I_5  V_23\n",
              "1056  21  28  0.404643  0.252682  2.903955  K_22  E_29\n",
              "820   15  41  0.368699  0.230041  2.864381  K_16  I_42\n",
              "881   16  58  0.360851  0.221764  2.847098  R_17  A_59\n",
              "142    2  26  0.363327  0.207895  2.814072   A_3  M_27\n",
              "999   19  50  0.338385  0.197754  2.786229  I_20  G_51\n",
              "1278  27  37  0.284180  0.188942  2.759123  M_28  V_38\n",
              "488    8  45  0.289913  0.156609  2.630593   E_9  P_46\n",
              "990   19  41  0.294071  0.143232  2.560453  I_20  I_42\n",
              "1156  23  53  0.197730  0.140188  2.542782  T_24  G_54"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqG93dC12CKx",
        "colab_type": "text"
      },
      "source": [
        "## Explore the MRF"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6BsheyNx3ID",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "###############\n",
        "## FUNCTIONS\n",
        "###############\n",
        "def plot_pssm(v):\n",
        "  mx = np.abs(v[np.where(v is not np.nan)]).max()\n",
        "  # plot\n",
        "  plt.figure(figsize=(v.shape[0]/4,v.shape[1]/4))\n",
        "  plt.imshow(-v.T,cmap='bwr',vmin=-mx,vmax=mx)\n",
        "  plt.yticks(np.arange(0,states-1))\n",
        "  plt.grid(False)\n",
        "  plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x,y: alphabet[x]))\n",
        "  plt.show()\n",
        "\n",
        "def plot_v(mrf):  \n",
        "  # prep\n",
        "  v = np.ones((mrf[\"len\"],mrf[\"v\"].shape[1])) * np.nan\n",
        "  v[mrf[\"v_idx\"]] = mrf[\"v\"]  \n",
        "  plot_pssm(v)\n",
        "\n",
        "def plot_w(mrf,i,j):\n",
        "  i_idx = np.where(mrf[\"v_idx\"] == i)[0][0]\n",
        "  j_idx = np.where(mrf[\"v_idx\"] == j)[0][0]\n",
        "  w = mrf[\"w\"][i_idx,:,j_idx,:]\n",
        "  mx = np.abs(w).max()\n",
        "  \n",
        "  # plot\n",
        "  plt.figure(figsize=(w.shape[0]/4,w.shape[1]/4))\n",
        "  plt.imshow(-w,cmap='bwr',vmin=-mx,vmax=mx)\n",
        "  plt.xticks(np.arange(w.shape[0]))\n",
        "  plt.yticks(np.arange(w.shape[1]))\n",
        "  plt.grid(False)\n",
        "  plt.gca().xaxis.set_major_formatter(plt.FuncFormatter(lambda x,y: alphabet[x])) \n",
        "  plt.gca().yaxis.set_major_formatter(plt.FuncFormatter(lambda x,y: alphabet[x]))\n",
        "  plt.title(f\"coupling matrix for {i} and {j}\")\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LaslLXvXbFcw",
        "colab_type": "code",
        "outputId": "5ede545a-6b55-45de-c4fc-c1486095f29d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "plot_v(mrf)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oFyJD2yJJhze9w8wqJfWT9Nem97v7VHcvuHuhd/YCCQAAAACA0NB2n6SuZnaeJJlZmaRr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\nMIY2AAAAAChhDG0AAAAAUML+H2ZQPPCRGhNBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1116x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AEncuXvWbCex",
        "colab_type": "code",
        "outputId": "36802e60-ce88-4053-dc70-8898cf60c0e0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        }
      },
      "source": [
        "# plot top 2 coupling matrices\n",
        "plot_w(mrf,21,28)\n",
        "plot_w(mrf,16,58)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OA/6+aqGIuKlsDZxM0TqrKmf7j36GoWh5fL5CfBvKH/rDKFr9l0h6T0Scn1h3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\ngg==\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F8w9J/35ccklWVVkjS0TGCCIA0zKbFtsG00fo6OWIGfdvTI8vhVtkZtZ4TmRm1njJh5aS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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YpDHjaWNyzzx",
        "colab_type": "text"
      },
      "source": [
        "## EXAMPLE: ParDE\n",
        "ParD and ParE are an example of a toxin and antitoxin pair of proteins. If the pair of proteins bind, the organism survives, if they do not, organism is screwed! Mike Laub created a library of mutants that targets this interface and their measured fitness (survivial). We'll see if we can use GREMLIN to predict these!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xhvsmaaByIjR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%%bash\n",
        "# download the dataset\n",
        "wget -q -nc -O ParDE.old.fas http://gremlin.bakerlab.org/fasta_cplx_sub/641_1413612817.fas\n",
        "wget -q -nc https://files.ipd.uw.edu/krypton/design/Library_fitness_vs_parE3_replicate_A.csv\n",
        "wget -q -nc https://files.ipd.uw.edu/krypton/design/Library_fitness_vs_parE3_replicate_B.csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDIhzgIE0Nav",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load the alignment\n",
        "parde_names, parde_seqs = parse_fasta(\"ParDE.old.fas\")\n",
        "\n",
        "# convert to msa\n",
        "parde_msa = mk_msa(parde_seqs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YterPZgq0TEh",
        "colab_type": "code",
        "outputId": "7c876a10-54b8-4916-baa9-aea87a149390",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# fit mrf model\n",
        "parde_mrf = GREMLIN(parde_msa)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "starting 162282.2\n",
            "iter 10 89082.37\n",
            "iter 20 84431.68\n",
            "iter 30 81059.76\n",
            "iter 40 80239.13\n",
            "iter 50 79914.95\n",
            "iter 60 79801.52\n",
            "iter 70 79760.5\n",
            "iter 80 79743.3\n",
            "iter 90 79736.68\n",
            "iter 100 79733.63\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5y9RydG1ALD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "parde_mtx = get_mtx(parde_mrf)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ahzwEyP11gqX",
        "colab_type": "code",
        "outputId": "35858c79-c5d4-4445-bf8d-c815d2b47f83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        }
      },
      "source": [
        "plot_mtx(parde_mtx)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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CZND3ZlvXhFLqtpcLCCEdAOmENAIWu4LNfTn60tbxP3HO8XRlE0gD7uu/dhkA7/7aTj79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ZEciPPZOX3jE411Qarryrd6XRTx4OTgskT/+cH/jlu+UmvuWE2IbG+37iDsmB+8rROm96+7/H\nfewPue/DQiSm2/X51x/+BlOvu/eCnZP/UyIh23/2k3zlP9wDwKs2nb+nvGvj4Hmvgbg2546F7+Z4\nar55VuL1YkRfzOf/e3T2uzuJ01WVbri6aSybBocbHYaybtp810MBOn7MfAbTcMKEmVaAqkAx22AN\n5kw+dWSRMyseByb7EDGDh0+2eeVW8XAamkI3iFEVJKTwVLtHlEGd+otl0w2Y67nsGSzzdF1MFBtK\neTqBWBw7a5SwKpbOI0tt2bnaNGBxamX9BeTIklhoWsWI4w2fjQMW12UV5s8cWSKMUykwAnB0rs3W\n0RJlU5ciJn6ccN8b9vH2v3qcj7xD+Pvc9aGHKOYMFEWIXYhzEP5BfTuBpY7P5IBNy40ZKvbfE7N7\nJMdMK2AgL86p7UV0/ISqfWWVzKfrHaq2zheOCCjSdVNlagWDb54QAjEjlRxJmvLwiRYbssn41ska\nzy53eGbBlZYQXphww2SRo3WXTrCqYnl40aXrxzIhW3J9Hp3p8sxMmzdfJ7pijh9xZMmTbf3t1SIP\nPdekkjeZz4RCfuC6hEemu7hBJDc2Z2o5nCCm60XsHRJwgiUn4LHpDo+f6fAzt20G4NNPLjE+kOPW\n8VTe0ziFr8+tyOu/oQIzicOhuR5HNLE5WRkXwiyv3jIgk6CnFnoYqoIbJgxk3csnZhy2VQus+CHb\nMkXMRddja7nIRx+fw8w2zLWiyXw7ZN8leLZ97lidg9NiQ7RjyKbejUjTVHovbirnmeu4DFgmC5ng\nwommSxAlTBccKWxyuulzKHCwdZV79l0+7OXbLcYrOTkvjFQsdE3h6cU2N08JqMhjZ1bYWMnT9iKp\nUlizTRqegKkNZVYdu8fKLLY8Gm7ARDmThU9SZjou14yLxbbrRZnPmy03I3Mdl7GSjaGpUjgljAW0\ne0M1z0rmW2bqKk1XdOz6XaauJ0jxjW4gN77Dli6/59zoy/hrrkInCCnnDa4dEcf29dkG1w5XGcvn\nGMo6LcdaXaaKeaqjhhQxiZOU9//We/nVzx3hA/cIBbhf+ccj7BnJ0846iCA6vwVDl8W0OSfC0tQM\n6phZvQQh2wpF2m4k4YlxmtJyQgljvtxoOAGGqsp5fVO5wOt31ziWwZOuESmoowAAIABJREFUGSli\n6iqn2z02ZzD/A6NVGk7AQM6Um7koTrh3zzhzTU/OH44fiWRHiRiwV2FVK72Ap5Za7B+qyGsECfOZ\nKu7mgQJPLbbYXCrwZJa0TJXyVGyD+a4nC31BHLOtWpKwfhCJUr0T0JmJGM+qy0eXOhQNg+GSKcfH\nVDXHqbojBVcKtk7HDfHChCVHzJNVy2S25zJVykurF9GB0LAMVVb2D8+3mazksA1NzuFdL+L60SpP\nLDQZsMT7tmTq0PElVHwOzrdk8WjAMhnJW+QtnVsmxHN27XDCSi9g+3BRwm0b3QBDU6h3fKlqDAKu\nerLZ5cDGF1eU4eUeqqrIrhSsQgxHKuuv5RdL3rwglkWFfqznR/atTODWRj9J+Z+H5nn93rGzujCn\n6w7XbKxIFcp+3Pdv3sV9H36ET7xLiF18/4cf4Vfu3sHB5dbz/s7sistI2fqWebE9fEy0l2/bPsin\n/+3dfOghIXT3H9+4BxDQ9r432oFN64uLXU4C99jJJtdfwDai78v3xKnmeb+59ll8sUVQzo21Y/ty\n4979Exye7bB74vKPsedH0jbs+k1Vuc73xXwuV9Sn50XYxctPya4Km1yNq3E1rsbVuBpX42pcjatx\nNa7Gt1G8bDtxLT+gllXunmt3mSjk2FjKc7Ituhebyhq9MGakZGBmELaqLSAvTTeWvmiHMyL9hgGb\nwaybc7LdI4oTCbkUHmiiNR5klbuxfI6TrdVqKmT8EkMnTZF8qZYfsnWgiKog4Wu2rpICeXPVWNrU\nFI4t9NY9137XQ9cUSpaGoSqy+nj9RIEvHG1SsiyambzzYMkW3nc9j3om6DFd75K3RBX1rg89BMCX\nfu52Xvm7D6IoCm7Weds1XkRRYKyccQU6vqy4BJE498mKyddOtrlmsiiz/LwhxEOmyleGGa7aOl6U\nMFbNrV6jFNyskj5VWzU/bq+RlPbjBC+7PyAgrX/32ALjAzlpkp6mKUkKfhDT73XmdY2iqbF5pCgN\nqZMUTtYdNmft87rnE8Up1ZyObQi8dsU0iJIEL4xlm33XcI7HZnpC6ju7L0u9ENNQ2TGcx826GZuG\nC1haBnNbA2EyNIViVrms9yImKyaqqlDL+BpJClsHLYZylrQi8MKYrUM5On5Mz8+Eb3IaUZow1w7Y\nWe2PK5Vlz+dVOwZ4ak48G/NNl7Fq7pKgjZMVk9kVUcXaW6vwiccXJecIBGxw3vEw1IBa1lWZKlvM\ndXxKpoGfecwlqYD85a8QzvbtFm0nlBXevl/XtoGi5DxFaYoTxAwWTCkFP1SyMDQVRREwMxC+XIM5\nk8lKTsK2v3qqjqYoknMXRImwCcgbErY3WckTxQntDEoHohOXtwS3ru+TWe/6bBjI0fUiCe8zNFVI\n3hsq+Vgch64pPDHf4tqp8yuSfS5xmgq4o6YqUs795vEaC12Pqm1KD8gNxRx+HHOi3WNrxpd6eqnD\nYjfEj2J+5R8FnPI33rCTD9x/DDdMKJmiormlLLpco5kY0bGVDp96ps6/umUjzaw7N1ay+dqZZa4Z\nqUorjLyh0fJCRktXNj+VLJ2mF7KlIn7fzGCv9X7HqpInjFNsTZdrQjmn40YxhQzeCeJ5/7unzrC3\nVpbdrSQV4iS9OJKdob44y76hilwjoijlxEqX7Rk8tt4NRHfN1tkzWM7uk0oUJzhRzI5aUd6XME7Q\nVYUVZ9VbMKdrbBnKU++I1zYOFPCCmKYTynGUUzR0bRWu2b+HmqpI39AwTthaLVDJG9KawI+FuE3H\njciZ4rsGsvV3uRtIqJ6pqzzX6HLH5mFmsq7mQtunaOmX1InbUM6Tz6C7u0ZKfP74ItcOV88SO3mm\n3iZNkZw4y9AEDcJetSfoH0vBeNlueV7SWNupcPxYdmwvN55b6JIzNSbMby33eXrZYcfo5cHeXr/3\nfI5v38z7fzx5RtoI/OcHn+OpI3W6XZ/vz+CUn3zXTbz7YwdZaLrszYSGzu1uhXHKgV/+HE//9ved\n9fozM+3LgqJeaqw1qQ6ihMdPNM769/IliKf80zPzfO+eC5um9+PcLtxaaX9A2p6ce12W2r7kv54r\nqvNSxJV24QDCKLmiLhwIqPdaH8H1zL1PLvXWFVRZ9/uu0PbjZbvjWuxGJAhxjThNqXs+pqbihAlO\nmBCnKadWfI7XPaabAdPNAEVR0FSFMysuTmYAXTQNhorCxHvJiVhyIrZVipTzZgY/StE1laYbS24b\nCFPsvrF3L4jpBcKotROEJGlK3QmpO6HkuICAYra9WBxvL+TQXA9LV7B0hdl2yLaR9W/mihOx4kT0\nAnFuDx9voiB4DUfqHlGSEMbiOsTZBu3InGjzJ0lKkqTUihbDZRs3iCnmDIo5g1f+7oN85RfuYGa+\nkykaqTx0eImOG8pz73oRr94zxHIvZLCgM1jQObHssWUwx+EFh9l2wGxbbAIS0itWHzze8BjMG8w0\nHGYaDoN5gwTw/Qjfjzh4po0XJtz/0AncIMYNhN9VxTTIGSrPLjg8uyB4DxsG8/hRQpikhEnKUN4g\nilPcYHXBnu36PD7dou2ELPUilnoRK17A7rEiXiQSQ1MVPJJ6L5Rmub0oYrnjM1bN0eiFNHqhuAYr\nDmGcULUMqpbBSFF4Mh1dcgiThDBJmFtxccJEbMC9iLYXiSTRNjiy6HBk0UFXFWZaAUEYy3vQ9mJy\nusrpjiM5nQN5g64vvPQ6fizhrwoKSSo4ig0vwI1iTrddHj3T5fhCh+MLHRaaLo1ucEmcuNl2SLMX\n0OwFLLoeQfYMeGEifLGCUHpctYOIdhBxeMnhVDPgmUaLph/Q9AN5TePngeR9p0U3iNA1oXBraAot\nVyR1/edWVxTmei5LXZ+uF0mRiryl0XAD0lRsvkeLNmNVm8WOjx+KOej68QE2lwsY2XfnTY0oSfHC\nGMvQsAwNBbEpLdo6ThDjBDHlnI4fxiiKguNHOH4kxX90TZVzha4pBFHC8UaPoq2L7/DF764XUZwS\nxSkpYnP/5FyTvKGRNzROtxxsXXhw6pqKrqlUcyYHlzqCN6jrFHSdfcMlbthQIE1hz0iePSN5PnD/\nMX71tds5utBlOGcznLP5+OEFWfgwNIU4TXnPLRsJooSJco6Jco6GE3DtSJXppkPbD2n7IZahktM1\nCa+83FjoeYyXbRYcjwXHY6RiST4fwLPLHbww5oNfek6+pigKE6Uc1YLBfNtjvu0RRAnXjw5k5tji\nfyMVG0NTUFEomzplU6flhBxeahPFomDkhTFNL+DGyQFURXAbS5bORDEniypxIniNThiza7BEEAkl\n3K4fCUEVTWW0bDNathmv2ARJzHI2D/Q96BRFCFtEieAQm7rKUMlkpuUy03LJmRorXnDW8QdJQiln\nMN/05DoylhXz+sIjaSr+v6mr2IZKzxdQ1zQVPpvTyw7HWl2OtbrUPZ9wHYPi9aLthXItb7kRhqpi\naGKd11Qxjm1No5DRHoJIzMNNN+RU3aHjR3R8ocCrqQo57bsziVsbJVuXXNjLjW2jxXWFK17qWJs8\nXCgaa4SN0udZi/7TF4+xqbS6F/vZO7bxG2/dT5Ik/MrdO/iVu3fw7o8d5E9/aD9f/vKzHNhU5cCm\nKq/54ANnfY/jR+clcHB5XMLLiTMNV6pPjlVXi1V93ufAPb990e+41ASu7YbnvXbuPbjueaCWwy8g\nqfpWx+99Wcz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Fxmp5xqo5ltpiAf3+Dz/CJ991E3f//lcBME2NB74xTa2W5423TAKw0A75\n1P2Hefsb9zGfQe2CKKXjR+wcuXIoSjMIWM4gpV86PM3r949IUuipZkDXi2j2AimOYRkax5pdirYu\nj+MVE2KiOjLf4/v2iUny8YU2hxccpus9GplnoBsk/O8TwgPuTEZWrVkeK92Ar58RRrRv2G5xfK7N\nrqmq/H5lXOHPP/k09969k8czDtiZpR61ssX4QJ7HFoWv3UwroOMnLLRcFrPNyel6T6rC9aPjhjhh\nxIllcQx1K8I2VI4vi3sKUDQ01KKJpWpsqokF4cSyxzMLLikpuSzhPr7kcM1IkaVeyHzGU5ptBwxY\nPk03lhPFV44us2m4KMfQBe+JG9PJ1C9rOZPHZ1e4cSyW92kw73Oq7TBWsKhn1yPJnpdnVzpMZYmH\npigstH0WnJeHme5LPT/FSSoTpThJ2VzL8/h8k50Zz6fh+kyUc2yuFCQkedS0cHyR2PTnp2reoNEN\nyOs6J+sC9jtRzZG31NWkKkyoWqLIcrwh4ORlczWh67/P1FVGCzaaqvDIvBint4zXKNrCrLmPzzcy\n0/rnlnoM5Fd9N0cK65Ow+wvO4aU2e4bKOH4suSdtN6TjRlw/NiDhzKqiCPEL26aTFZQUReG60arY\n9K9RCPzB3QJWcrQl4Md//PBJfuq2zfzCpw8DsG/jAJ9++DQdP+bHrhfzU9MN+aW/e4o//pEbZKJr\naiqLjsegfWWb0omC2HT2VT3/4OGT/OTNG+X3pamAQOZ1Xf7maMmm6YTctqEqiyrbhoqU2wZNLxAm\n7sBC08OPEhYdYf7ev0bdMOIVEzWZ/Hb8EEvTJLfLNlRmWgH3bFtVsKzmDf7D/zrC++/aJsfLn3z9\nNP/yhkk2DOR4ekGscUO2Rc7UUBUBjQIxTt0gpu2HTFTE+XpBjGWoUmkuSWHFEQlgPxEbLJqMxDZ+\nGLN1VGya55sejh9TtHQJrT223GXHUBEzUGWS5ccxXiQgW1MVMVecWXEZKVqSc3yhsAyNUlZMu2a4\nIg3Z+5zPdhDy9dllbhyrSZ7wQEEUtZY7viywDZctphvuJSlivtTxUs9N68VzC122jZ4NE3ux+IGX\ny6/72OPTAPzQdVMvyu+DGOPrJXAAt2T0iQvFTVvPF3X6/M/fCYhkBeAjj57h7TdskAXx3a9/Hff/\n0X/jznqPB/7tXfJzAzf/DCvf+MOzvmux5a0Ls7uUWCtiArD3336WQ79zvqDKerE2gQPOUlNcGwst\n7zwRjrUJ3Hrxd08v8Pv37T/rtYkf/wiz//Xt8u9NP/UxTv3xDwGr65RlfOv5mOuZ0V8oLtfE/fkS\nuC8cXpSJ/UsVV5zEpWn6LHAdgKIoGjADfAL4MeCDaZr+7gs5sMlijrGsqu3HMVXL5HVbR5htZ+o8\nJZuKZXC66WNn3ABVgbGSQclS2ZgtUhXL4MB4noWuQa1vGB3FqAiZeABNgVdtrhBECRsHxG9OFHIo\nCPxsf+K3MoEVQ1e5L+P9bCjn5cLfX2ivGaowXvBZ6q1ILtSB8bwUYDn/Wor/TlVNDFWhYgtFN4DN\nFWG6vXHAYiwjnyuKQpqmVHMGt20SlY5eKAxzN1RTqWQ4VjaELLauSg5cwVS5+/e/yhfe+z0AfPSx\nGQ4t9mi6MUczC4QDG8r88jtuwNAUnpwVm8a9YzlWHJ2xKzTw3FgV1ft+d+uf3ThO0dQ5OCt+c6Rk\nyI3hzkzFs5LT2VopsmOsKO0EirbODVNFvnaiTSvbnAwVDKo5g4alc0t2PXYPlnDChLYXM5UtVhXL\nIE5TKZ5QMHTedOMEbpCwmPEIpzsu1147gWWoTFhiDN2zZ5Cvne5StDQ2lsRCseKIpP327QOSd5HL\nunfjBZt6IczeF1GxDAazDq8TJlwzUsQJY9mJW3ICypYulAizYyvbOqausGsox7N1Mebfsn8YXVXY\nOWzLRPGGsTJJCiVLpReIz24aLtLNxA8uFg03OkvF68YNBUZKluxu7x+ukiQpcz2XA8NiolrxAgqG\nx/ZqSXbJNVXhmomilCX/Px0v9fxk6Kqc6ONEcL9euXlIJnYgNtzPzHdk0tL2Isq2jpEp/IHgp+Ut\nDWXN1DDf9LANlZMt8WxsGygyVcvx7GJHvidvrIpfeGGfiytk1rtexJ2bxPxUsDSSTNxiMeMk7Ror\n0fEivn5mWXbUarYpRQLOjfnmqpJd/7f652kZGvWeTy1vynOyDI2uF2HqKoNZl9cLE9wgRkWR4huj\nRaHa2HRDyYGL4pRf+PRhfvdNuwE4tezxio0l8obOXz81B8D37Rjiz955I4qi8MVTdQBun6yhKs8v\nznKx0DWVZk9wrAH++bXjpGnKN7Jk+MBIhdGyzQMnl7h2WFRM85YkhDtKAAAgAElEQVSGokDaTaUd\nRDVvUM7pPD3TkseS13XqnsdMx2fvyGq11QhEot5PrnVF5ZsLTa4dFnPYYMHknm3DwhqlKcbCouNz\n46Yycx1PChf97PdsoeEGrPQCqpktz6Lr0Q0jJis55rICXt3z2aoWyeuaLJ4tOz6WoUpER73tsHOw\nhBvEHG+JYtdQR4ikKAqyQ9p2I+I0ZetwgcMLovB57USF+aaHoavy+ycqOQ4ttmi7hhRG0hWVb86v\nMGAZwIWrz4/MNdiedSGDOGHHSImcmTCTFTS3DBYZLdp0vIiNA2Ld6HM9h0oWB7OkNk1TyrYuuSn/\nJ+OlnpvWi3MTuBcafXXLK1Hfu5Tk7fGTzedVP/xWx9oO2F0feogv/dztAHgRPP4vbmBiwObeP34Y\ngM/81G0ygfuPnz8KwC++ZseLagjeT+D+69dPAvDjt2wWv31wlnv3T1z08598aobvv2ZS/t3zIj5+\ncJb33L71gp/7468e56e+Z/U9/QSu/6x/8egi73rrjWd9pp/AwWry1nLCs3jhsGocfqFYK8Byui72\npc+XuJ8bz3f91+MRfuVoneunqhc13v7coTnu2XthcZILJXArGdJhPU7i5cSLNbJeAzyXpumpF+n7\nrsbVuBpX48WKq/PT1bgaV+PlGFfnpqtxNa7GFceLxYn7YeCv1/z9M4qivBN4BHhfmqYr535AUZR3\nA+8G2Lhx4/kHpio0s8qdE0WESUKvFzHTFRW4kYJFECc8t+RwzYSoMuUMFVUR3YWxkqionmj1mGkF\nnFh2pcy7HyfMtgOGMsVDP0o4XHfYP1ZlSwZnU1Fo+iFusIrJL1g6jy80mSrmZcXW1FXm2x5FU2dD\npiDW9ANaQcj+sRytDO6oKtD01odTDmeZ+EI3wNAUTq64UnmsYOiEScp8J5BcsdMrPiMlg5PNHp89\nJCrR+yeKaIrgrC1lFfeljvComqjlpYfaN55ewDQ1PvrYjLhx109yx+99hQ3DBW7eJKqizy17wtjX\n0mRH5ljdY6hgEMTrn8PFohfEhEm0qtYZqFRthXbG2woqFh1PwGP7ncRGL2Su69JyY2mI7vgxB+cc\nhsoWTtYV2FUrciYXkLN0jiyJCuFUJUfDEV5tS9lvDOUsRis21YyvZhsazy6Iik7/OF67fYCJWp4g\nSmhlx+pGNmVb48h8lzfvFJUVXVNwgphvnGxx9+bVSt18J2AwZ9Hxm9lvZFLvWadvY9Vi0fWJE6Ry\nX0LKk/M9Jgt57n9G3M89E2UMVWGmHUgu2tFllzs31lhxYk7p4rh3D5QZLJocqnd4alpUnfdtqNB2\nQglHvlBUbY3TK6s8om+e6XHdyIDsDodRQtMPSVNoZF23U22Xqq2z4Hj069qDeYPPHqqjXmEn5CWO\nF31+KliaNEYG8Xyv9MKz4FqqqnC60+OmcdFlKik6lqHS81fVI6cbDkVTZ67nsikzxg6ihF4QS/6l\nHyYstnzGSznKWUfa8WNhGxAmUrl1sGhyaLaDratUC2KMV/IGT58RZsjbRlZ5VVGccMuGQcl70tZw\n5s6NyYzfoq4Ii4NTjR5TWQVUQVRi/TCRYyGKhfLlc/Ue05nU/M0TNeHpZajMZVDdYysd4XupqnSz\n+el37j/Kvo0DnFoWz/GmQZuP/O0sb71pnLdlvLNTrR4NL6Bo6OwYyCB6XYeabUqO5uVGmqa0g9Vn\nJlEUirYuoY6WJjhiI3lLSvsvtnxpNyDvSxAz3XK4dqgq4fpbhgvkWxqqosjqazVvEGc+l0o2jzlR\nxM1jVVmZLed0vjHdwNI0WhnX78BIlThJ8eOYXsYtLJsGk5WcMAofFBBOTVHw4phnltocGBcdjbSR\n4oQR24aL0rR7uCBgl72m+K7NlQI9XyhbVsw+3FvnaKPDzsESD5wW0LJbJwZREDy4waz7vtwJGCpZ\n1Ds+J9uic7h1pMDNUzU6XsQTJ0XH7g07Rql7PuOFi/OmtlWKWBnv3FBVZhou5ZxO3lhVyWy7AqLe\n7wi0ooSRskUjs+UAGB/I8aXji4zkXnrfpsuMF31uutT44rOLvHrXxaFe60HRXmr/q8vtwl2JUfJn\nDs4CnNW96nuuhXGK40cUbV1yvgbu/CV2v/519P3jbR1e/dN/zt9/8J185qduA1Z96mpFk5+7Q3St\nXiqlzqXu2fzfV++4NNje2i4ciGv3E7duvujn1nbhYLWjdmhGPNfft2+c73se2fxgjS9kvwvX5/oB\nF+3Cwdkdq7UduLmmx3jV5pNPiT3tued3bqy1V+h34X77C6Jr+v67d7BnvHxJ4+liXbiLRWENZPOb\nJ1aeF+p6sXjBSZyiKCbwZuCXspf+CPh1BFLo14HfA3783M+lafqnwJ8C3HTTTeftHpwolgT4jaUC\nOUOjG6wKbRRtHSeM2TqUpw8CKpo6ThixezhP2cg2NjnBJds+vHrTByyTV20e4FRbbDAsXWWibFLO\n6axksBh7SMVQBbdjrQjBNcMVAU1yxSYjBbYOF2g5IVEmYmKZBlEizJI3ZtLNcQq13PoJUN8XaM9g\nkXnH4/ZN5VWp+ThhtGiwrZZjR62UvbbMYM5gNG9zb8YLszWNph+QAJMZJPSxk01evWeIjRWLjz22\nAMD1u0d44BvTHFoUA/mO3/sKD77vlbzqgw9xPIOvmrrKZx48wVvv3sZrtwwC8MVTy/SCGE25stbv\n5kqeXhhJT7LZdsiWcoE7dogNrhMmWLqKZWhMZH5npq7yjdk2QZzQzmZO29ToeCF7RvPYhviuMEnY\nUDVpeasLy6Bt4oYJO0fyTGTy34N5izhJZWLa9SM21WzcMGHL4Cr8diBvsHXQln5yeV1joRNy69Yq\ndU/c94mSSd5Qaa6BI966ucJQwSQFCd3dWrNJU7gjW5TCJGHQNlnshkxVs82aqXPNmIGpq1y/Ubxv\npGQwVrB4fL7DWAZJa3sxXhRz43iZobwYV6fawrB5rGTywzeJSeXZusv37q6RuwTseS2v0/HFs7Kx\nlGeq6hBECduzRaeU0/HjhJKp42Sck4IhPAsVFHZkHnz/67lFbtpYZtvAiyet/GLESzU/LbZ8znTF\n/HHrxkG8MKbnxxKuVbB1Om7EoG2x3N+45wy8UPDJpM9aSfx71TKpZ3zCoqGzabjAiewZ7RcCKnmD\n43UBcRsp2nS9CFVZhTau9EJ2jokNej85K1gau8ZLHJ3vspwVd/KWTj3bbFSyRSyKE5mwnBv97x8q\nC07flsGC/K4+tHuwZEmOzXTDxdAUajmTTVmSlTM1TjV75CJNFiU+9Uyd99yyEU1V+Gi2obp91zCf\nfvg0r9go5rqP/O0s//ivbuW+Dz/C8o4Msmhr/PpHnuS3/uX1vGqLKKB84blFnm10mSxe2eaykjdI\nUiS/6xtzDfaMl7h1QiysbhRTsgyafsCGzHssTVP+/JtnuGt7VT4bk5U8j8y2edPOUTpZ4tXoBuia\nKudoEAn3sWaHPYOrxH9DMyUHGIQoSMEQ/ppby+I5UxVw44TBgsVmWzxrbTfE8SO2DhQ5vCQ2VBtK\neco5g7K56me4caCApip0vVXBmfEBm6W2z85hcb2dIM4gujHDGXzfD2M2lgvEScruTPjF0BQJF65m\nPm5NLySMEyZrOXkOR+e75EyNoq3zpt1j8nocGK/KMXqhiNKErKbHaMmm7YYs9QTfFMS4itIEU9dl\nEufHiRT0uXFKrC+n6w67BkqSS/dyiJdqboJLgzu+IlvbLxYvJhTw5RRrkzfHj8hbupzDDvzy56SR\n931/9jUAXvujP8D9f/TfePxfCLumV//0n+P+z/eR+8E/5/3vFvy5GyfKvPWdv85XP/mbUtRjuePz\n0Kkl3rxvUtrSvJD4X88s8Lo9o7z/7h1nvX7wTJtbt63y/3a979P89c+8khvWJAfv/psn+NO3HTjv\nO41zhDYOz3bYPXG2IMy5/mn9ZOxSkg9TX+Xd9j93LtfvSqMvFnWx5K0f69k/rL2Wg1dIGbrcWCtu\ncqUJHLw4nbg3AI+maboA0P8vgKIofwZ85kq+VAGMrIr2XKvLcM4iSVNpQNrzYx6eWSFOUunxNd3y\ncDKvttFSZigbx0w3A2o5ncE1ONx/PLJMrdD3I1NY6AYstHxpTNhwA9qecGPvT2KdzNBVQZEbkShO\nOL7kMVq2pSDHkusRJ+DHKXNrSOpnmuuLPvQX9k6oMtsOyBkqtUxpy41jukHMUwtdKWzSdGOabkRe\n1zm8JDaSmwcsbF2j4Ya0Mq7V9ZurPFd3qeY0cpmS3nDJpFbLSz7WhuECr/rgQ3z552/n5/7+GQCe\nnm5Sreb44lPzDGbJiK0Lsnp8CTyr9eKbc222DNjSVHuwoHNkpSN97kqWRhgn5ExNdtj8MGaibNJ0\nY0rZ8a84AT0vYrEbsm1QTLiH6l2emukyOWAzn3nune44GJrCfDtgIkuCHltY4cEn5ihZQtVp0PZ4\n5GSTBx94lre95XoAuqMRn3rwJG9/zTZOr4jvcsKEak7j0FxPdqg2VS2eq3t4USK5Hk/O9hgth2ws\n5ZnOVCY7uZipCnSCVQGagq4z1w6YEY0zbt5QZKkX0HBDKWJyZMnlZMMXHcw13QBTU/l/HznNe79n\ni7gvmkYUJxxZciXHc27FxdZVtlcv7sPUcCLpT+ZGMf7/z96bR1ty3fW9n5qrzjzc+fbtvj2qW2rN\nsjxJsi1DjG2MB4gJEEwIGHgJJCxWHuY9EsJbQEhI8gjPz8EkQEzgOcTBzLZx8IRlW9iWNUvd6m71\n3He+98x1aq73x66z7+15kIQlUt9ed62+devU2bVr12//xu8vSlAUWMkik5XOkJKhs+h6sk7T1FQ+\nf6LF3nFbsjGu9kPGSwZLA+/SX/TNw0sin2pFg5SsPnIQkKRCZo1IGPww4Wh7wFTRlpvWaBMbbnmm\nI6KTibIlvYIDL+KZhY6MCo2ahp/ZcGWd8DCIsQzBAmlmMnEYxBhZI+8RY5qlqxxd6lMrGHKj9sOY\nWvZdI2Xe1FUZJb8QI5kYxYlc/6OgXdFUUdBpDQKOrwsDc1u1wDCI6QWhbEResnW2VwvCw5055/63\ne7fjRwm1gsE9mcJTNQ16fiwjLe+9Z5p3/+Yj/NEP38OfPLkEwBdOtnnV7TP82dOrkkhmT63MwmB4\nwzWZJzYGTJcd+hkhx3ylyGovkFG38ZKFFyYcaFYpbmElfeOeGvsbFRkNbQ9CGgUdVVWYzgyN5b7H\n2tDntsmabFje9yL21MpoqiINiz99bomnz/XZd5+4p2EUc3i9z6/+4SH+y4++BhAR0//09dP87IN7\n6WaR4MeWWrxu+xjrvYDj2R5x23RNMjOOjJsoTmU95ui5J4loGr61ftYxNZGJkBFuNYsmfS/isZUO\n37JLENG03ZDFtoeja3It1B2TWsHgzw8v8nf2TMprDQPh4BiRnWx4wTU3ZXc0XUatdU1EgjVPkVHf\ntZ5Ps2jJqAlAQ1c5stInJWUysuW8TVYtFlovK/n0ksgmuHqkbCvRUY5NIouRrjcy4AD+03ffAQhy\nnAfWBsxkTvI/+dX34XznbzH8+A/RdsU79vYPfRl936v4lp/7BOd+63sAwXQ523BetPn+1gMX9xgD\nzjPgAD56gQEH8JpL9CE7teay44LasgsNuP/jk4f5b3/2NCd//bvksf/81yf46Z/49+eRuGwlMgH4\n6vMbclz/z5dPAPAvvnXfZe/tRvGHT57lPRewdV4Kf/DEWb7r9quf90rCi2HEfQ9b0gEURZlO03Qx\n+/XdwNM3ctGKaVDOmj437ISSqXOuN5SGnQLcPlHhVG/ASk9sDrdPlTBVjT96doVdGSvkvmaZU+0h\nz6973JZ5EZaHHiudIRMloTioqsJUyaRoaZJ6f7xoMTb0SdNNwhJb01h2PaaKNlEqxrY68JmuOHhB\nLDfGnZUSZ/oDNoaRVKzvnikzV790s++jWepQwVBldKeaFai3fZGidXC6JJnNngkG9PyYMcdioiSU\nh64Xs3NCpFSOlUa0yil7xx3WB5Fs5J2mKW+/d1aSmLxqR5XjZZuf/JND/Id3HgDgZz7xHGGcMls1\nOb4hDJm9YzZFU0NXb8wrpygQJqmco9smynSDUCoP7aFIpSwYKosZAYyhqdzSrHByw+foqlBOHpwf\nZ1uzSHsYsToQQnG2YnFYdznX8qSQGncsZqsBC92QjUzZed1sk/tvn+bIklA2v33vBLfNVZl9151S\nwPbCiHfcN8+ZLWQO7zgwxkMnOxi6ypt2CO/lF8+ss9Aesmu8KJsXq6rC/nGHmmPIaN8jJzvM3jYu\n0wwtXWEQRfS8CCvzyhUMnV11nTHb5hvLIg1zZ8NmqRegKIqM6mmKgqoo3L2jKg3/hi0IJRa6vlQG\nZxoFmb55NXS9WCpUgzBirmae53ktmwZpmuJoolk7wPHOgKqtc9dEXSp6MxWTU22fqfLVGef+hvGS\nyCddU6XiXisYBHHK6TVXGkphnHDTeBkvTFjMvOLzY0XKts4Xjq3IqPruyRJnN4acbA241RFR2GPr\nfR5f6nHPjHhn6xg4poaxpfFxo2QSxlkK4KjxtipY0AxNlcbHiVWXqZrNQmtIOVNyxysWq12fgR/L\naOId0zVJWnEhnl5qy//PVYosd3zGMsdI34vwQxF9GXkWvTBmdeDTLFgynW2t51MtGFkUSIy37QXM\nVBzOdlzZyLvnRfzgnbOSxOS7D06zvjfiT55c4p23iUiOokCwvcJcuSBZOG8bq9K0TZneeL3oBiGF\noS7fq1unq3TckLWhkH+9QLSQiOKE4+tCdk6WbG4dr+KFCY8uiHEcGKswW7Y51R7IDI6ZksPpnsuT\ny23unhUKTRglRElKyw3kmL9l5zgHx0v85QmRsviOm6a4La3yaz94t2xVs9Ab8pOv28kzKx1pIL9m\nbozVrs8wjvn2fULBO7LSY8kdctt47Twmx73VEqqqSAPv00eXuaVZkWON4pT1IOZsz8XPUufHShaN\nksndel06Ucu2zmLHYxBGTBQ3DecwTrhzosZy5kzb3hRNvZ9ebUv5Me7YnNoYSLl5JTzf6bG/Id6D\njX7AVNYWY2SYWlnUUFUUKbdG79ttM1VOZgQIFUe0Nej4N9aC4iXCSyKbrgX2ZbI0rsaiGMUJ//jj\nT/Mb773tpRqaxJl18eyuRGE/coxpqnLVJs43iq0tBL740288j8TkAz/yAG03plYQ8/mJH389Xfde\nJqo2H8tYON97xxxj5Rsn/Bq1Thix/14rc+IounMqewd2jBV4zy0z8n0B4bw73RqgKlee519+235+\n+W37+eefEqzBv/jW/bz/NTv5oa9+8LzzTn3473J2YyhLf7YalluNt88eXubN+y9tjI7wyWcWL5me\n2XFDmQ4+igxeiwEHXNKAe+Zsl33ZdS6MSF4JXzu+cU3Mp9cKkcVz/fUoL2jVK4pSBL4V+NEth39F\nUZQ7ECkBJy/4W44cOXL8jSCXTzly5Hg5IpdNOXLkeDHwgoy4NE0HQPOCY9//gkaU4XRvIFMKk1RE\nIaZLNo8tCy+bZaj0wpA42WyyfbYn/jaqTQM4vNbF1EXRvpsVmu+tlXjjTQ2CKB2NGS8SNVmLXeHZ\nCcdTkjTFMlTpTfbjhE4QMuZY0qO3sybqDAx987znOz0MTWWlF7K9Ljwwa8Pgsp7uUuZJn6mYfP5o\ni6nqptemZOioisKZjsd05s0xNIWul2IbKoMsfXSqZOBGEW60SV0/WzV5fs3jjtkif3VUeIpvmSmz\n3A25fZvwbj6/7mHqKs+cafMzn3gOgH/99pt4z299g6Kl0sg8TI+f7VG0DXY1bsyj1CzoJGkq0yId\nXaMfRvJ321Dxw4T/+999nPf/xLvEOaZG2Ev44L/6CP/yF39YPqu9YzbPrQxlvaEXJ0yUTU6sujIV\nc3Ew5KlzfcqOcV6D8v0Tjqy5GxFD7J9wOJWlP46IWyxd48CkSIc61RFpk7ahMczWkGOoGJpKxdZk\nfyLb0Di67rGnFtHPCjm2jxWYKNj8z+c3ANjVsBhGMdNVS47Dj2PKxsURLENTGC/qsil9GIvehmv9\nkDR767pBiKYoDPyIqYxY58TqgJ3jRem9vxIqtsaZLM13GMUsdEPumVZk/0HH1OgMQ6I04VhGOV63\ndQZBjKIg6fMHQcwgSGQbjJcDXkr5tNAeXtT4fvtYgZUsClCyRY2OpipUs5qh9Z5P1w1RFUX29Hvi\nbJtttQKaohJmtWd7x0rsbpak5zVJhZeuaOucywrvTV1E28YKliRB8sOElhfSdEyZujZZtbB0laKl\ny0jZ6TWXoqWzOvQkeUrHDWXGwYUYNdSu2AZfX9hgX70sz61mfe7Wer6cD8cUKXYVR5ee8kbJJIhE\nL85RrdhU2Wa55zFWsHhkUbwfe2pl2sOQt2WNck91BtRsjS+cbMto0XfcOsXPf/ooNctktiTe0YfO\nblBzNGrWjUWCt5fF+zKepWMWLfH8JrLaU8fUaA9D3vIjH+KTH/5HgIjAtt2QB7/vl/jqH/wcIFJP\n949XONUasL02asIes6tSYmHgyprJ5Z7HkusxU3TkvqEqcNNEhdktNXdJmrJ/osxK5nnuh6FIKzQN\n9mZ96E6tD9AVlaplyBYQNctgEEVUHENGpqqWwXLHp2Bpkpjr9okaY2WTp5dEbvdsuUAYJ+yql2Qt\n5jCIKdumKC0YZcIoos2FaIUyKi0QdWgdP6SaPQc3iOl7EcMwlu1ZTvUGzJUKRNeQmj9ZcKTMVVDo\nDiMcU5MEMSJFUvz9yKpowbGrUcQcRuiaKhun+2GCH8fsqN14n9MXEy+lbHohuFr9m66pfyNRuCBK\nrhgZGmErVf0nnlnk7Zch1ngh+NzhFWCTLn4ricndMxXe/qEv84kfF20Hao7Gj33sST7yfXfyd7Oo\nzz//1GHu2Vbmzuk6N01d//obkXhcLQJ3qf50Fx67VBrz/XvHOZplJ41wfGXArkuQjfziW/ef9/vW\nJtZ9T6Q0XwuJy6gG90oYReFG++IoSlYtGBe1KHghON7uc0umD1/uvi+FFzMKBxc3BL/mz72oo3gR\nUbVMyqZB2TQYhBEdP6Tjh7S9iLYXEcUpNctgpRdStjTKlmhsWjRU9k0U6PgRHT9iR6XIMEzYVrNQ\nFbFR2obGWj9irGgIxsUo4WTLp+/HNIu67OkVZMX+I+UjJWVvTaQBnmwPOdkeEkQJrh+JehGEC60X\nxLS9SG6CAKamcCQjKrgQuxsOuxsOK4OQN+6pUd2SFnBorc94UbCknW57nG57dL2Yu7eVWB34jBd1\nxos6QSzqBQu6xv4Jh/0TDs8uDpgqm/hRiqlrmLrGIIj5088cpmiqFE2VNIU/f+gErbZHGKeEccp7\nfusb/OEP3c2pdY+KrVOxdZ5f7LLUcqkXbszuVxXBNBalKVGa8uhSh1U3YL5uMV+3pFHwvh/7jqyv\nlag1avsBd77rrZQslZKlomsqf/H0Cl8/vEI/EPWCUSyMw53jBVpuRMuNSFLx0s83LMaLJuNFkxOd\nAc8subSHEe1MGTA0heMbPmVLpWypzBQLHF/ps61mstwPWe6HtNyIuZpF1RbEBG0/oGhq3DVXxlAV\nKqYgEKg5GgVDxYtjeQ/NgkEQJ+xuWuxuWlRtPeuPJOrIVvtCqT/TG+LFMSv9kJV+SHsYsbfp0Czq\nuGGCGyasuxGLgyHzDYvVoZf9iKb3b9pbl2v3rh1VJsoGJfPqz2quZjJZFj9NWxC/hHEqFGJHY931\n8eMYU9WI05Q4TTnb9fHClGfWOgyjmGEkGoY3Czrtl1e60kuGmmNQzX5cP6Y3FI3qR3IqiBJh4LgB\nBUvLeoop2KbGTWNlXD/C9SP2jJcIooTZqoOiCOW4bOv0hqHcrLwg5lRnQKsfUHNET0RFUYgSUV82\nDDbTyw5MlWl5ASdbA062BgyDmLYbyo0QoB9GgoHQ0FEQCRymrvL0aueS9zrXKDDXKLDu+jy4a4Ly\nFmXi0HKXWlEQtZzsDDjZEd9560yFc+2hvIc4S6V2TI3dYyV2j5V4erXDWFGsuaKhUzQEWdVP/fcn\nKJm6XL+/8NEnObM2IIgTgjjh5z99lJ9/y14OrfeoWyZ1y+QrxzZ4Zml4w+yDqgK7x4vyvX3ibJu1\nfsBk1WayauOHCYaq8rEP/ggJKQkpZ1ouG0Off/iBH6Rg6RQyQ/l3Hz/L7z66gOvHuH5MlKSULJ39\nzQoDX8x9mgpCq+mqTbNo0SxaPLLY4thqX55TcQwsTWWh7VEwNQqmxs5aiUPrXWarDu1BQHsQsOEF\nTFQsipZOyw1ouQGOqXHnTB1dU6gXBFlTyRbsqIJJVPyrODpBJIy2XfUSJVunVjBQEIb9yLhfbHsE\nUULbDWm7IX0vYrbhUC0Ycn9sD0POdFy21Qqc6bmc6bls9MU78ebdE9QLJvWCyT2zDRpFUzaavxJm\nqw7jJYvxksVYySRJUvwwpmzrlG2d1a6PFwkyliQzehe7Hl4c89iZtnw33CBmrGDRcv/XkE83isYN\nkDpslS0vFswL0toeP9m+zJmbuJIBt9EPJHPkteBQxrgIsKtZZFdTKPb1V/34eee9932/wJOPnKDr\nhnTdkL/3O4/x+z9wJ7/x8AkURUFRFD702w/x7z959KK6sxcbFxpwAPu+493n/f6dv/k1tr3/9y86\nb+/U+X0Epy8gHfnkM4tcDSVb57mF3nnH1i8z59dioI9g6Op1pTleDQutoWQhBXjHFoKbazXgrhdb\n9+gXGy9NEvGLABEdEzetqwpFQ6cbhNwyJiz4MBabqrdFgFQtnWEUoyowvmWDMFSFYkGjl9FYrw58\nFAXJRKmrCrdNFTF1lSNZ7dXdk6I+RVMVvKwRaprCiusxW3ZYdMR5fka/a2jKeePw44R9Yzan26P6\nLoVt9Ut7KEb3ea4TsNIL2T1myWO2odL1YmxdZW9DLLCnVnoMgpi9NZMnVoSwGfgJCSm2pnKuk9V1\nzJY4vOyia4qkM225Ed/79lvkeEuWxnsf3M3nn1pitirmrPgBODUAACAASURBVGip/MBHn+R3vvc2\nPvzwSQDedsc0/SBmuXdjm6AXpXz+1LqM5Dm6xpob8unDwgO/o+lgGxpBFEvCGcfUmCsXOLizwVdP\niPt8dUbX/sCtU5IVbcRiqioKrYy+/N7ZCq1+wBlLZzLbmBxN41NfOsn992zLzhfPd6XjUTAK8lh3\nGOKGCcMsynlg0uFrp3u0BwHvvEnkcXf8DmtuRBClMip7Ys3l3h1VSoYu2xgAkiIbYMMNidOUkqnR\nztafqarc3CwTxIn8nKEqnGr7rA1CdjeFQC0YKrOlAl88vcH920W+e8kw8GPReHwUHem6EW6YEF5D\nfdBCN5Te+7YfEGesbqPG9LeNaZiWMPZHNaqa0mehG9CwLUnwUy9orPRDitfAiPm3AaMoLoh1auoq\nfS+S0ZGltke1oDAIo00SEEuT5CAjT2KaEaIULU2yCK4GwgkwimIZusrNk1VcP+LQmngPDmZNow1V\npZ3VJVYtg9WuLxT87LN+mKCpogn2aBwVM2MjrRc4uSEcS1VMdlUv3RDYz+TfsXafIE7Y1SzJDcnU\nVLws4nhwUowpStLMIWbJe/CjhG4QMl6wZGPsWydqnG67WLoq66MW3SEf/vt3STKVkqHzyz9wJ3/2\n9CpzWSSnZpl88Esn+In7dvLICWF4/pP7d9LyAk73LgpwXBP6YcSnjizxhqxdyMDXWR14/Mkhobzc\nM1WjbOuc7IYyOmfqKo6p8fZ9Y3z5lKhju32yTpzAj9w7J0luOm4oGGwVhfWMMGiybPPkiS5N25LO\nPlNT+Bd//Aw/8ZbdAMxnSt/znR43a2JuHVPjTMdnby2W7/e2coFTrQHdIOLW7BkMfOFUSFIYZPue\nG0XsbpYwdc4jMjF1lSDLJmgPAvw4YbxkbbKl2jq7J4scXepTyeSTrikstT1aXiDJdppFkyYmy12P\nnVmD7rmmw1PnOgz8TQXG9SNWBz5l8+re9MXukGY234MgomBo+FEi9/K5msOEaQkjOstoGfgRPT9i\nsmRtIUUR67SYk3m86Bgp2Fup219sXK3lwNWILa7XOB0xS8Lm/Xzp6Np5hlKjZPKVP/5XfMvPfULW\nEX7k++7k1x46wT+9f6dsRdD6H+9nse3x8LF13rT/+mUTwKefXeItN0+dd+w/fvk4gGzSfWSxx77p\n8yNcX/0Xb+bhY+sAvHZPEz+KOfuf/95Vv++/fP3Uec2/m7ZF/VU/zmc+9gvAZr3dF55b5Y03bbZY\n+sjj5/jlmc1o3Yjpca3nv6C6wBcTM5fRw19KvJQkQi9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dYlc1Y5YqbtZ6gVBGDzSLrGfr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+SJwfNGQVy82F6qtqEIAkzfxz2jpEx/ZpmcpyzwrF3zYr0Niecx2ZvCVIWUqM/Ot/HWbb1o\n2z7OUqK8E4RIy8SoebZN2mWGEzou1NvYmibHsdS2cHCxicGUjD156u3TIt5mq42bo4jwRIoN56aC\nuMWajabrIaDnOhCqEHjiAwcQBcitvoHDxRoErtsmNJZX8dR0Gdvp8bZtHyfKDfhhiMlct02haFpQ\nBAF1ysvR6gKm6xbSmog89d+aiJJYbNp4bp4EzY9O9mO60UGPpkKiG4BTxSZM30deVdim4MhSHabn\nQ5dI4AkQRcqm62M4obHg6UK9jaQkYnshiRZNNpQtB1lFxukyWVTzqgJZ4OEGXY5ZFMmomC5SmoRj\nlFeZkAVkFQUtz2Pql/MtC68bzqPj+ahQb0SOIwGi6QU4RL21sqoETaSclmsEcTXTZQvcuJvAfMfE\nDiRwknoe/l9v2o4/fW4G7z0whDpVWnX8EE3Xw0hGZ15YNdtFsRMiKUtrgrjXKnRJwPYMaS0byKho\nL3rYUUgygYjn5soYSujY0ZvA+RJ5RguGAsvzkTNkvDFDOFSWGyChErGhWAXV80PS+kaDANP3UdAV\ntBwfYzQgbJik/cX1w65QCBVM0RWR8V7zSaIeKAkcmjb5vozIY0c2CdvrBuZ+GEG8QkJvW46c50LD\nQl9CgeOF2DdEnr0TC01ocoQ7+rJYbJFx5AXEh+xHdvezTf+35sp4ZHIALctH1STHZvkBBpMaVElA\nqUPm076EirrpdZNOQYi/enEeD23PYB9tsaybHryQtCHGAVUUAT0J5aYNdVuWj7m2iRHa4ifwHLKG\nhCLlyOkSSWhNVdss8D08DdyxJYVvnKlgS4G0D1aLLoqmjbATYSxNlTn9EC3bR0aXWGv+QocoLA7o\nGuyAHHOfTloZvzRF+IDft7UAJySJm3iT+rWpIg70ZqArIpufDi/VUFAVZDkOy9QfNSlLUAQew7lu\nq3i8po1mdZa4sdwAYRR122N1BaYXoC+lsHsgCjwUgRzDfIvMCzlVQY+hoOMELNg7U21hf18Gwwmd\nzcM9usKSgUeXyby7LZOgQea174vAd4PcjutDE4lRfbzGPf2H78EvfPwl/N377mZCL3xAnoPt+QRL\ncMSJNfMqRsivJazeqMatjHHw9sLFKu7ZeuVW+1cyeItxPfzHGDcikHK14C3GRuezOoD72KEZxv26\nUez8j5/HPz72wC1RTVyN61EvXKN67G8cyDleAD+I1vCh1+P0QhNVy8X92wtrku4A8MFPHwcA/OmP\n7rvsc186tYzv3722nfBWCt6sx2o+43rE6pFXCuCmV8g+9HqVXK+GxRqZg2sHP3LFQG6jAO5m8aoN\n4i41O8hR3k/RtGFIItqejxMrZPJ+cCSPsuXg06dK2EptAVaokeepooXBFPnsjryO5+eaqFsehijf\nKK8peHq2jrZLFp/JvhS+Nl3G+3Ij+NSpZQDAD+3qxSdPFvG+A4MsQ9rxfJyeb2JLymCZ6HiBbNkC\npilh/1S5hf6kjBfn29hGN7Q5TcJXz9fwtj2X+7ocr5CF0fYinC/bEHkwzlPdcfGPh5cwktOZsInl\nhai0XDz2OhVfOEc4IWcW6khrIoIwwukiGUS2H0KTeAymZBxdIBvJYn0BCVXEDir32rACuEGIuhVg\nukqu3x//9t/i9kd/AHvHcyyjf2SqjAd392F3n447t9w4SbdiOwiiCE/NUEGY4QROr5jI0MnhyHwL\nW7IGPv7CImy6EdvXk8ZT02X81J3D+MCnTgAAfvK2QfzLkSKqTRtvP0DeV2x7mK+YaNsexnrIed01\nksAzF+swFBG7B8iDqYkCPn92BbNlcp929mj46PMLGFhlXvzwRBZ/8/QchgoGFBqA7x9KYK7uYL5q\n4RfuI3yYL56vwHIDFOsW3nv3EADgL5+exa6hDN4aRvjsKbIR84MQW1f1e4foYDyn4hvnakjpsQgB\nsaK4z/NYdv3Z2RZaVhWSyEOmx5FUePToLfz72SreTXvfj5WamLQT+PyZMhTKg3I8E6LAIaco2NF3\n9arEv54poUQ5VW/ZGeCpqQYEjsNxajo+ZOh4/UgahxZrsOmYP1m0wAN4eCvZpMXXQ+CAvqSEN0xc\nm4v33Y6kJrHkju0FGEhoiNCtptzen4UmC7i40mH8oDCMkDWIBUCssgt01QLjiT2pifCCkFXdeg1i\n5tybVFChnyskFSzWLSQVkSURduSSqFKe23CeLPiWG0AUeDheiD7Krax3XAxkNSzVLLaxS2kSW8jW\nI+bEDSRVVuWLua1bewycXGqix1BWJckknCu30JtW0XHIOXTcEA3TgyoJyNIqkOGHrLol8+Q3Pnum\niNcNZxC1u5u+h7ZnsCuXYgHhm3/y/8XPfuj9eMdEAX5EXvvn4yW8/84hZLSbk/IeLejIJ2XUO10b\ng3OlNiapgND5UhuOH6LYcdCgVfT+hIpvnKngTbvyeI52U/QnVJxYaWCh6WKQBoSzzQ6CKMKyyWMr\ntVxIKCla3ZIAUIESj/DVttAkmR9GOFdrQRF4xoeezKVwvtZCv64xPuqufAqGIqDW8TDRQ473TKmF\n4ZSGY4sNyHQOb7s+RtM6TMdnVgcZRYbthximtjSuHyKjSah3uoF0EPo0ceTgoe3EYmW+aqHcIQqe\n8Rq4rycNDoATBNiW73KCO24AVeQxWSDrhiIJbDN5LVQtl1XTgoiojz49U2ZJyKQm4sM/uh+1tguX\nzuFly0FWlZlgF0D4O7oiInK/N4K4q+FqAdytQiyE9J1CvL6LAr+GTwYApyhf/Vr44KePXxa8fPyx\nB3DHqgAue/djmHjkh/H8f3mY/eauX/scLvzRoy/r+K9Hfv7x08s4Qnlnv/6WiQ0DuTOLLfzD8SX8\n/juJlcJS3YbjBTAUkVVKVwc6iXXB3gfu6srjf+V0cY1R9vfv7l8j6AJcf0C+/p7cDK4mFrJe/n89\nXongDSCG5qutFNYHcOvtIl4JvGqDOIHjVgVPAfKqgpeKTVYxabs+FEFAQRdRpQpoIUjLZKlhYxuV\nwR9OaDght3FpxcUdtArWdDxoUrcdL4qAvE5I33HQJXIchtMyayUBiELZgKFCFnjUaKYxlofnOcDx\nqaqYxOPwQhuqxGORioXwHNhiuh6lFjn+lEq8hUazKopUhTOMIuwZTMILIxygg+PrU3UMZIiKWZNm\ntUcLCQgcBycMkaViJzwHnC2acIOItQXdMZLEcstFhrZT5g0JTdtHUhFwfoVMZr/xWz+HhMLj+UtN\nbBkg12P0riEkFeGK53AtNF3SphMLvRTbLiZ7dBxd6rB7EEURHpwoMBGTIIywPZ3ABz51An/+7r0A\ngG+erWK8N4G+tIZTS2TCemRPD47IPF68VMMyrYZeMiRs6zVwodhhwjF9CRXzlQ7GqeiIIYkY60mg\n3un6mgwndfA8B88Pkady2kNJBV88VoQkdtvIdIlHveNCV0VWbQUAy/VRsR3WJsrzArK6yPz2dEnA\nVMXGYKarQqdLPDJ5BQLH44lzVQBERTSfkGH7Efqpp5/th2h7ATS5exw7cgaGUhoWaybz7jpxqYrx\nwRSc8NoblrQioEVFb3RRgO0FmGl0hYGcIMChxSZ29ujI0ormjkIE0wvx5EwND9L2Ex5k3BeMb5+S\n2HcScQAHkCqOKHB4amYFB3rJ9ajbHrYWDCRXVcU4jkPHIR5/43Qz35sigdnFegf7B8hnq22XqfoB\nZDOvyQIapoce6mMTgYg7cRwRmQDI/DKU1mB7AcugigLxx/T8rtUGx3E4PF/FYEJHsUGCrLgdcCPE\nio2iwGPZtLE9l8ACzTb2p1VsKxho2z729tNKWcdDv6HCdHzWcrorbyCKyGIdL2KaLGCpYaPheBig\nnRPv3jsAgeeYqmVKE2H6PrKGhMOLpNL8/Kf/b+iKiKdnVphYxi+9fgsTOroZ1DouTCdgiZCVpoOt\neYOde+wD98YtPUzExPVDbCkYeO5CHfdRFdiDFxuYyCaxNR3hm7OkLe1dewbRtHycWGngHJXe98II\nB/oyuFTrYISKp+STyppzEgUeu7IpFE2b+e3piogL9RYarouMRl7LGRK+cbGErCqzqqzE82jYHmSB\nZ234K4GDhu2hYrtIybHIDdjfARJgnVxpYH9fhmW2eY7DYFKD6Qb45gWSnBpKakirEvwgYq2vrh+i\nbRGLhLgiOkw97L58YRl7aOfEoUsrmMglmILq1aCLAptLdVFAxXbwwnwb/TqZr1UnwL+cKeJdk33M\nQzNLVSz/7cwS7h4gG+zYdih1M/1srwGsV827EipUiXTNay0H+Rv0z3o56Nj+VStFV0JsRbARVvuZ\nxcHCXIXMTb/xlh3X9f0bVZ/iAG6mTL4r3rSvFsd4uQHctRB3Y7xz7yATMYkRB3Lx3/ePZbB/LIPf\n/PJZAMBvfN9OAMSHL6YNtW0fe4ZTlylSrhf8eOtk32WCMjXTu+w+vHCxijvGMht6ysVYqFrY1pe4\n5lhbHyStBrMHu8Eq1+dPLOIHqVjN//jmBfzaG7ff0OdX4+f/6SV89VceZP9+x/98Fl/4hdexf8dr\n3x9/awq/+MAro+K9KWyyiU1sYhOb2MQmNrGJTWxiE99FeNUKmzw/VWel3DAiZtBpXWIZj+19Bpbq\nNlqOzyohQRjBDgJ4YYgCbfOSBB6LTQuG1OUQZAwZx5frLJvXlyY8j0JSxkqrWzmrdFyMFwyW3ay2\nXUgiD1XiGScppUuUuM7h0AzJFOdUGYZKiO2xqIbjBbCCAA/vurw/+zCV8a/ZLgxRpCa5tJLDcWi6\nHgq6wrgHVctFTpMhCV0jYZHnmLHratGVWIjApJUbP4yQVSV2bU2HECxrpsvMg6OIiMh4fsh62Z0g\nRE6X4fjhTbVTnlnqIAgjximzvAApTWLZ3o7n487RLM4vt1GlrT47eginobmqH+CNO3P4xEsLGE8n\nGJdwrmWioClQBAErFqlg7h/MYL5KpKbjLPpIRkfb7rYS5XXCZ/TCEFVaWY2z0ilZwmyLjDVDFNGX\nUNG0PYxSAYBS08GlZhsFVcG2AskMPT9fgSIIuGMoiwrlZ8w2TewsJJn5dywg8exCBXvzJDOdocIE\naV1CiVZGmq4H0/eRlCXmjagIAkZTOjpuwCrSfhRhvGDgmZky84jiOMI3umc0h/5rcOKeOFvBikV+\n88BABqWmg7GCjoUqqbToioi0JmKl5TLBHNcP0bYJZzVutWtZPkodByMZjQkHkGN5bYoHfPV0Gcm4\ngqkQTltfWsHFcleqvWkRWfPYK8Z0SeuytEq4yHR8WEGAvK6w9huAeHrF/KzpVgd3D+WIXxfNUhYb\nxF5FlXj2/Q3TYyJPJq38KrTjIIyA40XS8lfQFAykNPhByHhJbceHyHMbGmUfmyPVo+l6B8NJHX4Y\n4WKDVMFHEjokgVgMxGMeIP44M/UOqybGc1BvQmHZ3SAkQhjeqra6mu1iWyHB5mrTDYj9i+2zKhnH\ncZBFHpYbMPEQyw/Qn1JRbrl4YOLG+Snnlk3YtJ0RIF5m/UmNfX/HDZCiVjQxp88LibF6f0JlrYF3\nb03j44fnsb8/yyqwB5fruGcgi7QuYbFBnqvJ/iRWWi4kgcPZMrm+eVXBYEZl1T8rCJBWJEQRsEw5\ngwttC3cP5KBKPC5QPnTH93HfSJ5VXwGSEV8yLYynDNYSP0/ns2257vO52LQwmNLYZ2NxlOcXq7ir\nn1xHTRaYCFUsJNOyfZQtB726grPU1kAXBaRkCYYkYqZFngNDFLEtl8DxUh2j1KhelQRULQdjWeOa\nIjRfPV2GT6veQ2mNcty7lWPTC5A3ZCw0LAzRllBR4NGyPDh+yCTANVnAcstGf1L9nhA2uZWYXumw\n9rOX64EWI66KXc2K4Gpm344XMA2FqSJ5Lr5dXKwbwbdL2ORKv31sbm2740eevojH7t96xc9crzE2\nAHz66Dx+dD/x0Y0r8bNlc8PxcanUwXhvt4XR80MEVCTwWvjkkTn82IGRq74nDCPwPIcXL5E9+UBG\nZS2psejPWIH4Cp9fbjN60Wq8EmbfN4rXlLDJswtVDFMOR8V2UWr5eMt4Aefq5AbIIo8L9Ra+OdVA\nX4r6vSkCDFmA6YVQBPIg39aTxqlyC7LIM+PtHUESCy0HhxbI4vPeA0P4+6ML+OB9Y/gCNc/e05PA\n8VILqiiwhd1yA8y0OujRFNbuOGhosP0QOU3GskkWaNP3YTdCXChbzMNOlXhMV50Ng7jnFkkLneuH\nSCgC2k6A/b1kg99wXZyvmjhTNPGWHWRRPVE04QUR7hlO4vQKmfycIMRwWsaFso076CL16ZeKGM7r\n2NWr4a++dgkAsJ+SfO8YIe85sWSiZXvo2D6GKXdnR0HF4ydKAIBHb6e+I16EI/MtvG1X/qaCuBcW\nquA5Dgt0g5LTRMw1HCa2MJSWMZ438JeH51nL5s8lVJwoN/AvR4qsBXLZtPCe24fwk39/BO/cTc5l\nvungyekGdIlnPMf5lo1vnK0im5Bx3xiZtL4yVcGnvj6FH36ITFp9SQnLTTJJXSiSsfD2fT145mIT\ngxkFafpEGTKPz51ZQRhF+Kn9pOz+/GINK20fptfCo3Rj88JcC6MZBarAM8EW2w/hBAFmaXCW10WU\nO8QwuWaS+55QSBJgwFDx7BzhDOYNCctNF1PFNjLUGHrPYAKzDQsXKg4e2UnO/WKjA5nncbpkIqWS\n37hYddCXkLAlY1wziFs2bZwokmNVBB7PzjXwCNeHT1Nu6CM7e1Fs2XCDEDMN6gNWtyAJHHYXkrhQ\nJRvVY8U2ghDor7XXBHGvVYRRxISGBC/At+Yr+NHMEPqTZM6K1fcu1NoY5cnGhOeAvpRCOGV0g59L\nyOA4DklVZIqmlhvgjsEs5mnLYo+mYKrcwXjeYIEBQOYUxw+hSmSTmzEkNEwPUQTWjhRGwHLDgsIL\n2EdVIUWeQxhFTCkVIGJNQbhxQi/euPdqKrIJGa4fYo9M5ieixgocWaxjZ6G7OWiYHvwwYh5lfhhB\nCiMcKzXwzskBAMCnj8/j9r4sUpqA//bkRQDAm3ZkkWpKLGEw1zBxaLGJnC5iiF7bXT0p/O3hOQQh\n8NMHCB9VFnl86UIJD43dnCiD6QbwqQIoAOQ0BecqLSaKNJjWSODStqEJ3XbQomnjxAppoQSAjx+e\nx0/cMYx/PrqEfX3kGu0NkrD9AKlIRChzbFUAACAASURBVIryu86X2ljsWBhLGtiaJc/Lb375LOod\nF7/3g3sAAKdLddzdn4Uo8Cw5NZhQ4XhEiTFuXy23HKw0HYgCD522Ekk8h135FPwgZAHP9nwSqsRT\nRWcq9tSbRNv22eZJVwScKjbx5vFepjYacxlrpse8VQezRJjqRLnOzOx396ZxZLkGP4ywl4rQtGwf\nXhBispBmG8GWRUSR+OswD57oTTBv2FrbhSLxsL0QVYs8ByMZEoD2JVSWuPBDHwlVRMaQcbpI9gsF\nTUFakVBqOdiD1/78tBobmTkDa/3RbgSr+UOvRAAHvHwfOWXV5n998FbruMgaa9fBjbhJJ+Ya2DtC\nxu3q9tOJX/0cAODjv/TgZQImH/jEUdw3nsaP7CH7gowh411/+QIcP8C//Xy3je5Pn76ID14lWLpR\nbNT2uh6OF+DMYospbh6ba+G2kSRmqw56Kf/tSgFcHJzHz2zux/8a0fJF1J74LQDA3x2axs/ctQUA\n8PUzdK+4yj8vTixeaXysDuAAQBJ5XA8Rw3T8awZwAFEr/sbZEnb2knl5MKvh7GILOweTlx3TRgHc\ntwPxenuzJuoxXrVBXFoTMWCQCYbjONxWyGCm1cGxJRK0TObS6NNVTPa5LMjKaRLmGjY+9vVLePT+\nUQBALeWg5YS4MN/GwzRDW7RsnFuxGG9OFHj0JiS0na4xdFaRYcgCJIFjPbYdx0ePpkDhBZYdLFsO\ntqQNcByHhQYJCMIU2bBdLJu4bZgMogtlm2UA1iOunEkCh5rlY67uYjjl0O930XICbCto7DcX6zZ2\n9OoYNDS8uNiir5GN+75+HefLZPM3kCU8BtML8Yb9ZOOUVHicW+7g+Ut0cUspmOzTUWp7qFMeytmS\nhXLVwhv29ePfTxBex7FTJfzYw9tQWrWJvBG4QYiK6bOBGyLCjryGZSrUEAduaVXEuWVyTpLIww9D\nVJs2+miWdTydwE/+/RH8w08dwB89dYl99txiE9sHUqjRymRWTUKWeFRbDryQPKRv217AVKmDRSqf\nf2Awgbm6SzYB1Cg7r8pomC4G0gpOU3GPe7ekSCXVJVVNAKiaPi6W2uBpBRQAmpaPiyFwR3+a8TQv\nlU1sy2mYq5PzrHR83DOcxKGFFtsc160AQ2kZHc+HTXmVz1+sI6VL2D2UYsdbanl403gWM3UXyzSJ\nIAtkQ142ffi0rOIHIQ7P1HGgP4nbRrob641wqtRh46/t+Ygicq8MhV9zXwyJGBgDhM9zfLGNgYTC\nDKmTioCGHaypqryWUdAVVvXiOQ7v2TeEuarFlD5HkjpG8jo0uZsEkkUe1baL3/76Bbz/TpKxjCKi\nVHVqqclENBZrNoIoYkIkTdODrghomh6TW09EEaIIEPiALbQdx0dvWl0T6NVMF8MZHS3LY4GjLgtQ\nJAFT9RYmC2TTMt+wsNixNpR8j6vxsRVJpe2yCp7tBkjp0poArm56yBoyUprEeArzLRM5Vcbt/Rks\n0fG8O0eCDJ/n8Iuv2wKABHt128XJBWqUW8jgByf6wPMcZurkeZypdTBTsfCBe0bwTycIf/Zzz8zi\n99+zn3YmXH3Mb4QoirDYtliVSuA5TPak2AJLLAd85DWFPQe392cRdiIsNF1sTZMLsr8/i38+uoR3\n7R/Ai7TDIiGJeHKugu/Te9GgFf+BpAbTC1CxHRZ4/eobtmKpY7GK2S6qCtpxfezKptg9KHUcpDRi\nswAQyxnLDeCHAdvYTdc8LJtEHXkoReY2h3IlU5rEVGWLTRsDaRVLTfJdosVhZ0+S2gyQa9N0fWQM\nGcttG+mIjLWji3UYkoh7h/KYqpKEadv2cXt/FqWWg0UqltRjKIgiwhWWV3FnjizXiZJv79WDiLmq\nxTacHc+HJpOKdZYmBzRZgB9EKCRlHFsk9yWjyDiyXMO+3gwK1KYjqYrEssX53pifViMO4GLbmNig\neaMAbutj/4KLH/mRb9/BXQHrqyOPn1rCo7cN3NR3rQ/gAKwJ4OodFxlDZgEcsNa0+dwfPrLms585\nvgAAeHTfEP78PfsBgCXgAOD55y9i/i9+HD/+t6RC+qU/+dsrSs1fL9Zzz64UwMWVpzvHs/CDCP9w\nfIkFcRMDJIAbzSlrqki/+eWzjBsXY3Iotaa6Wf2nn8VcxWSCXHEABwBv3tV7w+dTbbvXtLrYCNcj\nChJzBN+0sxfeKq73zsHrWxculjrY2vvKCJ1shKMzdewfy7zs4C3GqzaIG050JxjT89FwPAgch3vp\nprSQlPHMbBNVy8c+mnk5utzGvUNpPHr/KG6jrwkcGfg7+3RkaBZUFQTcN5pkWdeMLmEgKUOXBQwk\n4jZM4uGTpq1uAGkDmW11sLuQxhYY9Ps55Gh2+s6B7gbIDQO8dWeOKWv1GV1Po/UYpItU3fZgyCKS\nioB4qRkwVCryAuh0w/zwRA7ljgtDEbF/gJzn1pyKpCIiLUto0QU6o4nwwgiWG2JXL5m4bS/C2/cU\n0KBPsemFUCUO2/IKVjrkgR1NK7h9yEBGldBLRTV+7I5+SDyPtndzNWRVFLC/V0eZ2jpoogBNEFib\nZxCSYHqiV2XVAYHnMJlL4+0HfCZiEkUR3rm7gD966hJ++cFxAMC/nywB+3rRtAP22R5NxUhWgyEL\n6KOttQlZxOu2ZrBCFeh2ZBI4sthGzpBY240dhNg7nMK2vIrhjEw/J2BrQcfxhSaaLvnsZK8OgeeQ\nkAWmihRFEbbmFFRtF2MZ8ps5TURalnEPrXyKPAc7CDGeV2B75C4Pp1SYno+JXArHaOA40Z/ASEZG\nww6QHSSf9YMIPbqCPX0afKoEKHAcUpqEXb0qSm1ybL1JCfeOJVm739XQckKm7uoEIbbkiLXCw1u6\nFQ03DLDYMbGXbvhrtsusO2Ivs7pVhybxrPL8Wocs8kzJsO2S4LdsOxhLkXkhY0g4NFtF2/Oxv58s\nol+/WMKbt/bivXcMIkeVSYttG74ZIaNIrPJhBwEG0xpbgDIG8awUBY4JqlhuAJP6NsYtyVEEnF2p\nY3s+wSortiuQKp/bDfYsN0AQRtjTk2YJqh5dYUqPV0KxY6OfU6FKAs7TFrqJbJJ6WUas3Xs4p2G+\naiFryEjSitoYb6Bpe+A4jgV2ksBDFDicqTSZcmHD9jDRm2TKiLYfoOV6GEhp7LXRjI5fvX8cSU3E\n7bQ16Ed+9h7wPIepWvtGbiNDw/YwkupWA4KQ+MHFwY7pkASULPIoqPR58QKMpQ0MJnQmYnL/UA77\n+tJ4cbrJOhZOLrTx4HD+Mon7IIrQo61V7RtPJ7BEbQL6kypOl5tQVomTNDoeRhI6UZ+lY6hpEQuJ\nl0p1phCaliVIPI+0IrHg6aWlGrZnkjhZamAbrf5lNAmOF6LH6IoJzNVNDKd1tGgiKq/JqHVcjGZ0\nzNN2UF0UkdcVtCwfOYV8tul4KCRJ8vN0lQSwYRRhLKsjjCIsNGjQqci4fSCLM6WNk5qrcXCphrcl\nSDfI+VobeUNBw/Vw1yhJyM5VLCy3bZyvtrCrEAvCcDA6Iiqmw9bf46U6enWV+cd+r6HUdFjwFmO2\nbGKUKic/fmoJb9s9cEsDuI7jswrNtbC+OvLwRN8V3nk5LpXIGrq+2nMlZDYI8mKsbh2N8ei+ocve\nt3pvN/8XPw4A+JN3ESGU/PtIAFdpORi6CXsmAGsCuKthdaXQUEX8/jsn14iY9KaUy8RO4gAu9mDW\nFRFRFK2pbgKkWmqvUnddrFkoJJVuC3rFvGJFdXWrJXC5V2EsWLJcty8bpzeK1cmija7bJ4/MAcAV\nK3pbew2ililefS/z4Sen8CtvIOIk8fOzEYoNe4066/rxFCcsnOv4zY2wKWyyiU1sYhOb2MQmNrGJ\nTWxiE99FeNVW4lquz0xVNVFATpOx2DGxmrYxmU9D4HlmkvvQWA4nyy3kdYl9VpcE7Ok14FISOkBI\nmmEEHKWZQMcP0aEZUpdmMm0/RNX0iQkvjeYVkcdELoWq6TI+TI+hoNbx4Pkh7IDKyIsiwogalTok\nSz7XcDCa3Tjj03ZJBmRLykDNcSDyEXKUi2GIIvwoQrHjYIJmKM43WsjpEmwvYN+viwL6dRUixzNR\ngCiKMJSUoYkCjlPe033DaRwpNpkU/M5cAl4Y4lS5jSFaEbSDbnvdTppZSUgSViwbe/LdloMbgSrw\nSEgiFjskG5tVJDTcrgdQ2XQRhhHqVoDRbLcaChAfuEeov95cy8R804Es8KQCB+AH9vTiox99CfuG\nkvjaISL/ndNEtN0QAykZX7lAWgwenRRhKDwGUyTL94ULJbxuNI35po3ZWlcgJsYJ2rr78PYsUqqA\n+8YzGEhS/y0/wNZciJbTrV68Y7IARRCgCDxO0mxgVhfwzHzXwHyioGHAULHUsZFTybjKqzIMUcRi\ny8LYqkxd2yUiGP1JMhZKbRf/84VZvGVHlrUa65IIjgOSsgBL6R6LJgqMj3Q1vGNHAS8u0zYkVUTN\nDZE1ZBylxuH7+7NQXB5F08FJ6md4eL6D2wZ1fOzgAv7zw0Se+cBAAgtN+6YySd+NaFgeM0yWeR4J\nVcTxcgNJWi1P6RL29KdRbbusbeLNW3txYaWNtCKxqpIuiujPqODQbavOajLSuoSDs8QDcjhJxGyS\nSpc313J8LJsWBJ5j4jK6ImLfQBonlhvMaFqTBSxULcw0O9hB2/M4jrTnCDzH2mPO1VqYzG/MX4g9\n4cayBsIwImIeOvW/TMjgeQ6LVYv5DFluAF0m7Z8xdEVAT0KBKvEs4xtGETKGgr1iGgu0wrOrP4Vi\n3WaCKOM9BqptF8W2jUHanWE6AfwwQsP0MJEn55RQRZQaNm4fvFyY5Xog8zwUSWDHJgo8Kh0HfZSH\n17Q8hBHQcT0M0NcEnoPrh5htdvAuyon5/Jll7A2SSEgiTlJvzj1DCfzq587g3ZN9+J0vkqz4I3cP\nYSglYzCp4a8Ok/ase0YS2FtIY7KX3IfffWIKv/7wdlRaLvMgDaMIbkjM1F+kz+juXAqyyOP1w3km\nWNW2faR50kUSr5lv2dEHN4hQsVzMN8n1Hsvo+NzZZeyg8/y2TAL9SQ2SyLN2YY7j4AchTpQa2EnH\nCM+RCqDAc8jQCm+5E+IXPnkUf/DIHuzvIfdhKKehZXmQBJ5xCSWB2AcNJa/Nx/qh3YNMMIfjSLVg\nJK1jngovFVIKeJ7D8VIdT9Jq6PmyjUcmevCfPnUM/+un7gAA7OvNEJN4/nuvnRIAvnh2Ce+7e8ua\n10ZX+ZdeqYpwJXRoJ8+VrAA2qsoYirjGkDrGtaovcfvZ9eJ6K3DXg+v1D5M3qPisl8r/dto0xFiq\n26zStlrEJG6nVEXg/j94El/71Tdg6IFfAQD8zH/+Bbxnbz9etz2P+3/nGwCAP/7xA7hjPMu8THf+\nx8/j7H//QQBgz+JqPHmOWJG8YYLs21ZX4TZCbBvQn1GZYB3HcfiVz5zE//FGcsxX88q7WhUwRvZt\nv4fa4x+6Lk7d9QisxFU4oNvxtxGKDQdfuUC0Nv72qVl89VceXGMCHled11c+rxev2iCu4XpMbc+Q\nSHl3ZzaFKaqKVmm5KJo22q6PS1XS6503SPtg2w6wPRt7y4g4uNjAbM1Bb4K2Nm5VcbTUhEofvJbl\nwfYJAfwMFQqZzKeQkHmoksA4ISttB34UIqPIOLZARClUkQcHDoWEjLmlmAcgQREELLdc5lEm8ByO\nLW5sphvHDSfKLURRhKrpYyclyr9UquPLpyp440QOCzQAWmi4UCUeI4mAca1qpg9pnEcYRThDzSvD\nCDgdWJBFjgUnJ1daOFM0kaEtgPOai+GMjOMLbZwRyff3JmUk6cZwyiPnFIQRDFmAG4S4Z+uNB3IV\n20XJdGHSjWrd6iCjCbhIDcbLbQ9vnwCeOFNGgW6S3rSlB0XTwv/P3puGSXKVZ6Jv7BEZuWfWvnR1\ndfW+qFtqbaAFS0YshgE8YPDYY67xApfrBV97vIHNvdhjzONljD0z19t4jC++xhhsbIwtJEvIkkBC\naqmlbvVW3V1L15aVWbln7Nv9cU6crKqu7q5uSSAz9T5PP6CszMjIiBPnnO/73u9956smXqC9it+9\nvYDHZ5qYXGwBBwkX+y/+4jj+6oeO4B1/8izylOcv8MBC1cByw8JbDxBq4FzbxGOTdYxQhckjgzrO\nrRiYrTnoobTRPk3FV+tVLNRt5CnNotRx8MxsE64XYi9Vomy5Pr4520FSEbBcJNfowckaZIHH23YX\nmTH7w6dX8O+P9OM4vffPLXQwUfRxfN7AbtoPMlW3oAgcRjMa85MTOA4dO0DN8pkB+FzDRS4h4VTZ\nhNhPrkfKDxGEEpbaHi7QXsgoinB22UTmgATg6o3nz5WaeIluNkf3FWC4RHny2Dx5bSiZQNv1sDOb\nZEmQY3Md1Ewf33u4jyl9nlsxsdRykdFes1PKKwqR59iGOZuQEETAm3f2MwpkveMiq0tIKAJT+CK9\ntyoROKH0iqRKEkUNy4NII7ucLqNhuNhBg65S20KvrsIPI0ZFHO/RkTMk6IrIAsKm6SGhiJgopLDc\niudEGWEUYV9vhgUossijkJLRtnxGBZ6IUuxerkcfDc5W2i4yCQmOFzCV1sW6hQvNDm4fLjDaOTGo\nj+AFEVuQF1oWBlIaUZT0Y/VcoNy0sdCxMJwix2sYLhw/xIpNzj/RFCAKPFYsh6nFjqeTSFLKTxz8\nRU1iBL3cdDDec/1iDQLPYb5hMoXDSsdBWpHQprudtuujN6ng61M17MyRc91VTKFt+wiiiPna3TaQ\ng+0HeHyuiruHiafSz/zDWfyXf7cHn31uHm+5mQR7OwsJNB0PU40OfuRmQs/6yvkyJL6FHVly3993\nqB+WG8DwfOymwaomC6i0HMzWTRyk9OYgjFDq2GQd8ikFXBWx3HKQ0SQWTJuOD8MNkNdkFvifWG7g\nLRO9WDHJPFzq2EjKItoWxyhP01UDaVlETpGRomNeFDhEUVeFDiCJz9sn8jCcAA5NaEYRGbOqJDCV\nYEngcKHSYevq1VBu2oyCuj2jI5OQYDoBStS7VRZ51E0X37Wjd1U/bgkJScDvft9Nq14LYAfkt/+v\niPUB3MvFtXzcrrSh3sgr7Fr0uesJ4Nbjuen6ZWIk14ONRFHWB7CzKyYu1cn6fvfOnhv+rpeLX3t4\nEv/nPSTgiWmrzir6dhzAre6Be/1vPY6v/6d7cGy6iXf/7I8BAH7+3nG8VGpivmbh67/0XQCAn/q7\nl5BURaZsGQdwwMa9lXHwdiOI25wml9r4vXfu39RnNiOM847/+MANn9O1sNr0fD0OjWZwaJTM1XFC\n9+X2SK7Ga3bHpQhdM+6FpgVZ4NF2PSxRaf/RVICa4+LkkomBNHnIbC9Exw7w0kILGt30j2V9TFUt\nlJs2EjLZgM93TJxYaDPRhncfHGKCEnGgN9s0MNdwYXsBa+IPEWGmaeJQj8T6qtwgRE6V1yi7zTUd\nGG4A24sQ0A+rIr9GcGA14kBMEjjMN1xYrs8EEnKqhKwuw/JCpuxYNX24foiJnIaG1X1Iw4hkyTs0\nEHDcAJbro5BS2QbO9iPMrRioxYa7ioimrWIop2KhThbG6YqJ7T0J8ByHJlWPDMMIBV3CTL0rI349\ncP0IGVXE8zQ4GMkqWGy5WGqS78xo5BoO5BKotslrXhBCEQR0bI817H7PRC8SEo+JgTRTtjw4lMI7\n/uRZ/P2P3YqbP/EoAGAoLeNR20MYRlhuk3t190geX1daaNINVxARgZLZSgdtm2xUD/bpmCl3oMoC\nlmJ1wGQRpu2j1nbA0frLC4sdNE0Xtidgkd6rasvGSJFk7o7NkKpVkm6S4w1uQScVQtcP8Owsec/+\nwRS8IELJcNj9XGo6iCLSaxQbmI8VdRwZ1PHUpTazGJhqGjjan8Ncw4FDN/iG4yOMIjaGroa62d2E\npWUJM7U6WsMeC0KjiPD95zsWlqlozK4eDYWEiKKmoEWryB2HbM4vVu1rfud3ArK6DJWq3bYsIn9v\n2D7bmIYRkc9vWB56aQY27rOaa3YZBS5NHjUcF/20ulozXDQdj/U37e/PwPNDcEGIAq1GLTVshLRH\nK54nVUnASpsoj8WsAIHnUEgp8PyQLewN00ObKhLGvyGnS2jXu5Wz1YgDR0Xk0bI8WH4AySCfG8xp\nkKiheJUGC1ldQsf24QQhEwLKq+R6SQIPgSNjxgh8eCEHReBh095Y2w9QNm0s0OoLz3FQBQGH+rI4\nQS0SFg0Te9Q0sKoPJYqI+flMY+NE2bUgCTz6UyrK9HsFnsN822QV9D6dbDTvHs6z5yqMImQTEkom\nj5cq5Fm+eSCHdCTigUQvq5q+Z28fPvvcPH7wlmF87MFJAMCeYhrHlmqw/YDNDW+Z6F1jM5EQRWIv\nYNps3gkjYMm04YchjCa5jtszOiSeR8NxMSKT8THXMCHxPPwgxAI93yFdQ16XEUURszXYmUtBkwUk\nPdovnuBhukTQYJK+J68QW4iy5WCCI2topeXA9kKEiDBZJe/bkUvinbv7iV0KZQGstF2kNAlty2NV\n+jAifbZLxrXnijACSzjKIk/GlR+il/bwKZKAlCKi1nGZmMrrhwvoyyiwvBCzVCVYFon1wbJhA7gx\nNsn/qtioehaP7c1ULL6deDkBHHC5KMpy08YXqZjSj90+Bknksa2YYIn4K8nVA9hUn9VmsV7oBCAC\nNm1r7RyuKyJeoorXsXDLahGTR36GBHBHt2dgfY3MJ4M5DRcqHdQ7LgvQfv9dBzY8j/UiIJWWA8Px\n2ZoUB1dLDRsDGwTrcZV9dZUyFuBabYWwHvGcGVdAT823sH/48oR1lTJNCkkZf/4DR654vNXwgxBf\nOb2E9x65vPfx5eL7j4xe8W8khrh+sZPXbBBnegGjGWYUCQLHISGKyNDKlszz6E+oiKIOe4AGUjIW\nWx1YbsA2+F5IvCckgWfVpYwso5BUkKAbmDCMwIM8ZDO0MrS3kMJARka547AMYkaWsdzxMK9ZONCn\ns3NzfEJvsenAGkorWGo7UMWIbbYrhscyOOsRB3pZWYQsckirCiy6qem4ATw/xKW6gzupVP6lhgNd\nFtZcj4blo2p6KLU9VnVzAGiyiFyie5sPDmiodRzcRlXoJitk4Ss1HdyxPUOvfYi66aNu+pgoquw6\nGk6AnuRmhGA3+I1hhOYqKWtdEdi9AYCO7cH1QyQUEX5AJs4wAhqOi209SRbIVCwbHTdE3XAY3fKR\nY/PI5zXc/IlH8fyv3gcA+PAXT6MnrcJyA6a6CXo9SlTV7WJVRhQBY71JzFF/L1UQsHMwDcPpnmta\nFZBUJYgCzzbpwxkZIs9hueVgjIpZpDQJlRZRhIvVLj0/xFK7q+aXVUWoEg9NFlnGqT8lQRF4bEvr\nmGsQGkJSFZGQeFxY7qBIVQpXOi6OzUdQRZ7RhWWB627EaeZcEolgxEhqc7LN8fir2i7qHRdDGY1t\nvg2PiCZYfoA+unF6craJs2UTO4oeRjLkte0FBSuGhx79xsbHvzWYjg/HI/cvk5DgBxEUiWfBmRdE\nKKQULHds9lpOlzG7YqJqu0hK3esUV/NjcR1V4tfQev2A+DV2bJ/5eaU1CaLA4fxKhwVsB/syuNQ2\n0HI97Owhc4UscKgbHgzHZyIm+aSMtuWx/wbIYle2Nk7QxNUMWeThhxGKuoIGlXjneQ6mF8Bt2Ewx\nzQu6wWXsUWY4PmodFy3XZ8qCXhAhLQuQBZn93oGsCpHjsI9arNQNFw3HxXzTxC1DeQBkvjYcH1XT\nZZWzkFYpY9GR64UXhOi4IdtkCTwHyes+Z7GHpyoJyFEhDz+I0LA8jGd1TNaoSnDTQlqR0HRcVkH6\n5D+dw1tuHsTHHpzEr7+ZKAV++olp7C0k4QQhyrQK1q+rEAUeUw2S6Cp1HNw1UsDOXArnqFBIQc9i\nTzGFUttGklJ3FUlAMoiQlETmV9ejq9BkUgXeSRU3g5BULsfyCQzRZIAs8ii3HZaEjD0rRZ5jYiqZ\nhISELCCTkNj7NJl4kl6odpgC5FLbxovlFu7bVuwqr4URErKAhuEircZqroDIayhs4l55YQieTt9N\n20PT8XDbtjyWaMLRsH1wHAfTCZi9x0zDwEzDQBBFbFzt6EliesVg57qFjfH8dB03rwt8NqqebSZ4\nC8OIKdEO5TUmXnEjeGmOBBrfbvRl1A2tAjZTCXolA94rCZ2ktLXrb09agblKjCRGnNQfuusjePfP\n/hisr/n4wgduAUDmpg/euZ0lHYFuBSn2j/3qhWV84LYxjPfqOEsV0vcMptCTVtCDy5+xjQI4YGOK\n6UZqouuxnr66UQBnOP4aFc9f+qez+ORb91zz2KLA4x0biNdcC5sR7jEdHzzHwfKCVawG8ltuVK1y\nS9hkC1vYwha2sIUtbGELW9jCFv4N4TVbiduZTa2qgEmQeR5uELIMYiGlQDA4jOYUTNCSb39Cg9sf\n4s7RDEwqJawKPIYyMiSeQx/te9pR1HGv7zN6mMBz2F3Q0ZNWMEKl4QfTGgzPR1aVMKSQ49tegNuH\nMkhJEpOa12QBSkSKoHcOkQxWGEXYntbRdn0sUQPwobR6RTPde7eRDLPI8RhMKdBFEYPU2+dCrY3b\nt6fRk5CRotn7sZyH4bSKQkLBzau8L1KSiD0FoEIz6glRwGLHwb5CilH+dmSSxHKA9liMZDQUVBmX\n2iZ64oyqYSGrCrhtKM0YS04QIogiaMKNZZPGcwlw4DBKf5cXhpByPE6KJOusiBxUScCBfg1JmZxb\nTpegNQQcHUlimlZ4bhrMYr5tI6emmDx3XhMh8IRC+eEvngYA/Pd/vw8fe3AS+3sT+PRDFwEAd4yk\nMVFQsLuHfG6u4WJXrwpZ4KHTqkRSFnFwQEdaEWDRympOlXBoOImsKmI40+V/a7KJW4fTGKRVt+/Z\nV8SJkgE7CHDzUDcztzuXxu4CyUjyHIeO5+N1Y2nkac/nQErFbNNAWpWwt5d8brbuoD8l4dCAzoR7\nTD/AVNXGG8a6lK7DvVlIAo/7GxmIlAAAIABJREFUd3SzpxXLQVaRMJa/dlP2PdtyOE2rqwd7MnD2\nh3D8ELtoX5FMBWm4BMeMink0sas3gfGsxgQulkwb371TYubI3+kQeI5R90bCBFw/RN32mFS7Jgvo\n2D4qloNePbb3COCFIW4fLrAeisWOhZQsoeG4rGpwcDCDpulhph5TwUhVK5OQcJ5S1/oyKkw3QBCF\nGM+S7LbrhxhKJuAEAevDS8gCvCCE6QesD890AyarPUPpZklJxJUSgTn67IV0nkuqIquCl9o2NEGA\nroisL8wPQiRVEV4QMUPntCbBcHwMprsZ2XxSRtP0kE/KMCgFnJiHd08km5BQSMro2D7rN1xu24gi\noC+lMvqh64dwgpD1Yl0vwojM2/FvLbVsZBUZl9rk+qQkCb4QwfFDZngtCTwWDBNJJc36Vvf2p3C+\n3GECSAARMdlZSGBPMc28LX/67u343PEFHO7P4oHfeAQA8KF37sPtQ1lWTfzLY4s42p8jpu70d9ZM\nF7osIq/JjFJUNRxi96HLLJtreyFaJpH8j1/jAJyptmCUfQzp3fPrTSlsbfLDCAsdC7uLKYzT/t+k\nImCmYq6p3M63TBQ1BeP5JCMAKY6P8xUL/+EmFV85twQAeGCiD03TY5VjgNBzU5vsnc0nZNZ315dS\noUsipssGYzE0HJf5dGog//v4TAO3DCeREAUmkFPruMhqXbug73R4q/pTr4TY0Hk1VlfhnpmqYUdv\nEi/ONzblBRbPOautTIZW9UvdaBUOePmGyN+JmF0hjKJtxY2rgPH97WzAAIv91t7/0f8dP3/vOAZz\n2pq56dRCBzv6ksjd+hPsfb/3zv3sfv7sbz+CD3z+RwAAFUqL3nMD/pzrz7cnraB4FQGY2NNvM1hf\nFfvy49P45Fv3XNNi4EaxGfuMd/zh0/iv77kJQ3ltwwr3jeA1G8SdrbeYV9yZlQ4maCN9zKMfTGmY\naRnQJB5PXSJUk1tHIlRNHydLJu7aRkrvRVXFU5daMNwAHZdMbOcrHfzj6Qpupp5zthdgvmNhj5NG\nfF0fukiUD8eyOpv4m7aHiC72cZO95QcYSmlQJQFPzhGxk9GsihXDRantMSrjJc9BqbkxXelZalIq\n8ECp5SGriYioU5zlB/jGVBN37eg2955ZttB2QoxldFyokfOoGj5upcbiz1OhiqQs4IW5JmpjPl5a\nIJu/28ZcPD3dYAIjNdOH5YWQBA5DGUKROrnQgSTyqHdcxnfmOQ4pRYAscnjHpu7gWkzWTMzUHLbp\n0mUB+/s1XKK0mIRMvPH++plFjPWSyf6WgRxmGha+MdXADnoe8zULXztXgyzxGKFqRR03xELVwKO2\nhx66SYypS/t++SH86JsmABDVzc8+Pov940Rw4H039ePzJ0oAug36zy42cXqpg3RCZvzyvrQCww1w\nYamCfrohf26xhYrhwQs6TJHwq+dqsL0Atw2l8XVqpi7wHDiOY71iPbqEtCrgiYtNttj1p4hP4fOl\nGk6WSHDWMFwYroKa6SFFJ4cginDnaBqPzlSZN+JTCzUc6c3iVNnAeaqIWW7a2NaTREIUsbPv6jSP\n50stNjYyioBn59q4qTfLepRUQcCFZgdFVcElqpA3lldgeoRyFfsGLhsOzpYt3Dn68ibyfyuwvRCj\nVAHS8QLIIumpioObIIwQhhGODuaxQnn/xZSMYlKB4wW4aZg8zzvsJFbaLoaSGhNKWW46WGxb2E0p\nkTG11wtC7O8j89qjU2Xcva2InYUU2+AEYYShrAbTDdhz1rI8ZHUZuiKy80hrEpqmBz8IUaBeY5Yb\nYFDfWBAk9gvz6W8SBY7Nif0pFXMNCyOFBBMsaZoe6cVLysw7LjaZ1mSB9TwEYYT5tgmO4zBHx1Y8\npiSXZ++50GhjIptiwjolkwSOJ6Zb2J3v+i/qkoi2u3Ff37UQhBGeLzVxiH7Hiu1gf08GBY9sKLKq\nBEng8eDFMm4bIPduMKvBCUIsd2wc7iOvVdouFg0LphcwmvJQWkbT8XBsqYa9BXK+nzu+gPcdGcIH\nPncSf/ORewAAp2st/O3pMt62mwgxfeJNu1Fq2/BCgSVHLrVNInrFc4y+33I9jGV1nFtpY5R63XVc\nH2lFQq3jsgBNkQRkZAk9CRU1SocltKSQ0TBVWcD2rL6GujtfJ76Fx5fqzM8vq8gsUSGuGn8funUE\nlZaDQ1R0ZbnpQBZ5JGQB0zRh0PF8DCU1VC0Xewaunmiy3IAFsBeqHfQmFCRVkSliFn0ZJ0pN7O/t\nblZ/8KZBRuWNhWmSKhn/6+lm36nYjK/Y+gBuPW4bJ8nl9QFcFEUsiF6NeD37p1NLeOv+gWuKn1wP\nrnWurwZaFkk+XE2J8+xiG3tWJdKnygYGsir+57OzG9IubxSfevQ8fuE+ogQde9etD97W0wXjZ3w1\n1TAO7GLBqfce6MdLpSYuVDr44J3Ed/fUQgf7h5LY9lNfZgIcj09WcOd/fhRPfZS0q9RpAAcAd2wn\n+6mTl5o4OLqW8hobb1c77hUNymNs5h5fKYDbyM9vPV76zbcAuHrwtpGQzWZwpWei0nKYajMAPPzT\nd133sa+F1yydUpdEJCUJSUnCeE5DRpZJQz3PQeKJ6W1GlrHQdJHVRGS17kZ3oW7h5LKBk8sGVmwb\nHSfA3IoBVeSgisTEu2G4aNoBE+4wnJD0RTjk386cDpvKMxuOT3pKRAEvllvoeD4UgYci8EjKpA+B\nZEgF6LIAkefQm5ThhxHymog8PT832DgLON90Md90YXsRSi0bL841aWYYaDo+crqMthPADUK4QYi2\n7WGp5cL1Q5TbHsptD14YwfR8eGGIMwstnFkgQUZKlTC5bEAWecgij8fP16DJIlp2QP/52F5QIfIc\nFlseFlseUpqEPX0JDBcSiCIiGmD7IRqWzxQ9rxeTZROv25YCz3HgOQ6azKNi+OzaLtYt8FxXzcxy\nA3AckNFE6IqIC8sGLiwb4DkOuaQMxw3Y9d5ZJL0kcV+M5QbY35vAvl9+CKd/4wHoCg9d4TGaTODo\nnl7MltuYLbeRliX0pRVUWjZyuoKcrkCmG9T5qoFMQkImIWFXj4rzSy24foi256HtefCjCOeXWqyJ\nl8i1O2iZLmSBp2IPPCYXmqiaPhbqNhbqNsodD03LhyTwKDUslBoWWja5tz2aynjoiiSgYfkQeR5t\nx0fb8aHLAhEwsAIsGw6WDYeoY1pkzCRVCUmVyNefX2qu6au6Esodj90DJwix3LDgBSFOlSycKlms\n367puogARCAJgyCM1qjLeUGEMCI9nP8rIKWKSGvk32gxgaxOKiNELIRUBHK6hIZJgqisLkMUePAc\nsNiyMVMxMVMx0TQ92H6A6ZaBhCIioZCeyabrMdETjuPghxH7b9cPcUt/jj2bTdND0/SQUkWcLDVg\nuwHrWUprpI9psWUhqYpIqiIUiUdPWiHPki4jR8/vSlgxHayYDsIwwqJh4cRyAxxHkhO2F2IwrcIL\nQoQ0yLM9Ugn0gnBNMOAFISIAJytNnKw0YTgBBnQNk7UWcoqCnKLgkekysipRwHS8AJYXYG8hDVUi\nKoR108WgrmFfXxq39GXZPBxGETqeh4x8Y5XgU7Um3jTRC00UoIkkaOrYPkyf/FvsWJBEHorAwY9C\n+BFJfA0kNBQ0GdN1A9N1A5LAYVtKhx+FKGoKipqCAz1ZZBQJIsfRamGIw/1ZfOBzJ/Fn7zsIXSFz\n3KGeLH74yBBeqrTxUqWNrC5jKKNh2bQxlEpgKJVAShIRgQhvFZMyikkZu3vSOFdtI4wiGG4Aww3A\ncxxOrTRZf6Is8jAcH03XgyrxyCoSsoqEl1aaMJ0AC20LC20Lhu3DouNnoWNhoUMC0jCKsD2TZHNd\nUhFheyFUiYflB7D8AGlNgiIJsL0AJdNGybSRVIl1yqlyE8WEgmJCgSYKeKHSgC5de5Nft1227oVR\nhPmORebnmoX5moVMglxXwwnYuRlOwDZUIk+UmTmO9J6sVuvbwuYwVV4rFnRq/uom7W/dv7FdQcu6\nsQTLZlHruJhcar+sYxyfaeD4DBFQSmsSzpc60FURuiriM8dmLnv/6gAOIEbRmiy8ogHcl04usAAO\n6Noe+EEIPwhXJdTWJiheP0GSQat72/YOpeF4AZu/75woYF9fhonzBWGEHX0kgJv9/a4C5T27evDU\nR+/Drz08iV97eJK9vlAj86Ik8hinOhHNVdYysshjrmoiWLf3fYFe49WIe+tiHN/gPTHiNW/9NdkM\n6obLEolRFGGx3hWAy+ky/vbE/KaPFcP2QibSshqxCvCrCS6KNqb4fStx9OjR6NixY2tee3yyxioQ\nZ6otjKeTWDa7alYHBzKYWjHAccAF2ghuOCEGUjJEnmeUnP29aTwxu4KmEyCvkQt6c38eJ8sNLLTI\njfwPh4fx+HQF90/04plZUk1LyiKWDBt78imWvQvpIPeCkGU5+pIqbC9AUhXxYokMutGUjoWOSQMW\ncr4pWcLJcgsfuefyh/uRs8TfpkYlvv0wZD5g8x0LhufD9iJmVxBFEQw3xE29GZygqmgDSRUpSUTT\n9dhxJJ5DxfBxoCeJ8zUyEfenFMy3HIzQ7EDFdDGYVGEHAWp0ko0ioEeX4QYha54HCE1vPKPjjXuv\nXz72Ky8tY7FjM9pizfKYUAYAmJ6Po4N5/MtUBTpVFr17Ww/OVVo4VzXYb5/IJjHbMuCFEfoS5PMP\nX6jj6EgSy22PiZg8drKE77tzBLrC44N3jAEATi8a+ONnL+HJk6T6dvPuHuzs0cBxHBaa5Jrdtz2H\np+ebyGoCbI88G4f6krhYN8FzwK0DhG6ybNqoWA7Olm38wEGyaP3L9Ap6kyIGdA0vVciEFIREGEWl\nao8ZRUZekTHTNlhQP6hrKBk29hfS+MqFCrsmuYSIuumzRMO+Pg2TFQt3jxFxAwB4fqmOkVQCZ2tt\npnJ621AaS4aN148UcXD46hSWb5yv44l5MubvGs7jeLmB+7b34pEpUok+0puF6ftoez6jTV1qWuA4\nDorI4c4hkoU7W21BFQgV7u0Hu3K7HMc9F0XR0auexGscG81PZ5e6lK6lpoWcJmO+ZWIkQ7KjuiLC\ncHwUUwqjvSwaFnbkkkhrIluABnMaWeRWUa3jgPAiFcsYTpFkSkrriuHM1U0UEjJKHRujWfKdKVWE\nIglYatis8TuWpJdFnn1HMSUT1UBVZJUyXRFRbjkb2odcomJPyw0bKY14YMaLoOUH6E0qaFk+o5iP\nF3RUOy4sP0CCUt160go6NlHVlVY1cttegHxSZn51miwQGiYdbHGgoEoCsxMQeQ4FXWEVUIAqqDYt\n9CUVHNl2/Vn747MtnFppsYramZUWhpMJlj12/BDT9Q76dY3RCv0gxGzTwICuMY+fqXoH47kkVq+r\n/+P5BfzIzUMk6UZFTP6PP/4m/uYj90BXRBygz+iJuTZeXG7gD79GaE2aJuFX3rgbqsgzn7iCqpAk\nEc8z6mhWI4qTksAzRoEs8hAFHuer3epcXJ261DAZBTEpi7jY7GCCUnLj3ybwHKvAigKHpbaNvCqz\ntYXnOCQlETXHwSKV+z/ck8WnvzGNj79xF6MWvbTUhBuEGExqjAJ+z44eTFdIQu6ma1Tul1seE1MI\nowg8x0EWeSy1yWtpWUKJsnPiRLjlB8jIMmZaBu4aKbJjpTURy00Ht9Ix/p06N11BP+2qKLeIqu1m\nsZ46+a3ARoW9G/mt307cSHHS9teqLy7Wrcs80zZSq+zYPmN3AOSerb9fr//k15iNAFsfbv9JVoGL\nP277xK/vDe/5FfJCpg/1hz8GgFTBgOsLpIDNVb0uLhNa543A8QJ8+TShdb/7pmEM/+jnMP+n71vz\nHtsNoMrCZWqXwLXv1WbG3mpbH4AwycZ6dPzNC3N4zwYVwfg7r2dues1W4rawhS1sYQtb2MIWtrCF\nLWxhC5fjNVuJe+pCg2UNXD9ksthxRJtNSGhaPkJaGQMI/TCKIphOwDKKEYhkd8f2mcz7QE7FVMVA\ngWYB+jIqSg0bYz0JVtKNvW0Gsmo3xRdFqHZI9iCmBvAch5QmQhZ5zFCjcIHnoFOT37hfJc4wH94g\n8/j4JKmE5BOEgtmyPWam27F9eAGxMIh/U8P0kFRE5HSJ+WCYbgBdESHwHIv+w4jQUUayCZbF7k+p\nKHdsZKhIRcfzUUgoOL5cx0093b676aYBkeOwLauzeyDwHHK6dENmuo9P1pDVJDheN+PhrSqxG26A\nA0NpfHOmhgI1ZB3vJRn9uYaJviTpRVMlAX/23BzePFFEUiZpi7rtYq5tYneum4GvWDbsIMRoMsGo\nYvsGdfz2v07h7mHC9ZcE4olUtmykZXI9CgkZS20bfUmVSb4bjs9oSbFvSrnlwPNDVEwHO3tIpmi5\nSWwP+jIqq1S0HR8ZVWL9Ql4QYiCroml6a7I/SVWE5QaMeiaLPPwgwlzHZCIEhu+jP6XBdHz2bGiy\nAMMhFNL4eHE/27ZC4pr36uR8h/mADWTJc5BOdAUAcnpXcjwea7H3WTwmAFK99YIQKU3C7v4uV/87\nNdv9pRNLTFhH5DlkdRnLLZuJ3Hh+CI4j9yce87ZHaG7x/wLAsmFjX38abctnVZRMQsJyy2Em203T\nQ1Ilz3Y8PuJeAwBr5piVtos0rZYBcS8aqeDVzZhCQvo8G4bHhEfi46++dzH++RSpyg6mNPA8h5rh\nsrmT2IIIMJ2ATZPxXOUHEXutZfngONL7Gmd8wwho2C4G0xrmmmTuHEprqJkuRC4W8/GR12Q8MrOC\n795OGAA8BxxbqkMWOBzqJXOW5QZ0fpKxa4PfcC38zQuLGE4mWE9jFBGbm5gNEtOZlzo2hqk4U0zd\ns/0AI3S+Xqhb+L0npvAzqxgXMy0DlxoO3jLRi5D+9oenKxhIyzjUk2XfeWgkhZ/60hn80E3EEHy+\nbaJXUzHXMdFHBYRsn9A4+1Mqq3ZdqHSQVYnxe5yNbxguZJFHqd2d2xSJRxSRylpc0XW8gAnWAKS/\nbjSXQBiBmYT7YYRiSsZkpc388lRJgOUGOFNrYSJD5r+64yIlS8yeBCBr6+lSC8MZjc0pYQRYXgBF\n4HHHxNWNnE/Ody2EJIGH7QVY7thM+Ct+JlKahIsrlJHj+zjYl8Fcw2Q2DzvySXgBqQLHlc/v1Lmp\n0va+I3v/XquVuPW9aP90agkFVcHtO/KXvfdGKnFTlcsrbxshCKNrCsDk3/dnqH3uA+y/n5+u489f\nWFjjA/eRL53C9+7rXWPYrYrArf/5MTzxC28A0K3qza6YVxRW+Xbj+EwDR8Zu3Cj+lajErcdc1WR2\nFBt5Ld5IJe6aQ4rjuD8D8DYA5SiKDtDXfgvA2wG4AC4C+OEoihocx40BOAPgHP3401EUfWgzJ7Ie\n5xttFKkBc8vzUDBklAwbsw1CR3n7rj6crbbw3EKH0e8yqgCO43BstoV76OLAcaTvZ7bmYB9d3F+H\nPD716AW86wihfd2v9OHvzpTwfm0YD14kdLYDvTpKhoPXiQW2yeU4DjXLRcf2ca5Bgr3xdJL1EDw+\nVwUA9CdltBwfphcymt5gWkLTDjYM4ibr5Fh+NcJMzUFWE2FQwYiyaWOm7sALI+Zr13YCqCKPt0z0\n4p/Okw2WKHBISDyymsC832ZWTOzpT+KxmTpOLxDa5XhfCvWOy5ru+zKkD+uJF5dw902EFrinV8Op\nkol/fnIGH34HmZzmGw7SqoDRrILxnm3XfT9PVJo4WMzgL08Qo8zRHPneKjWb7k9JyOsyHrpYZRvQ\nH71lGIstG18+V8E8bYr/+Bt3428evYiLZQN3jpN7rCs8Hpus4+tKCxoN7CYKCj77+CyO7ullfV3D\nWQU/d+843veZ4wCA7z8ygM+fKOHk5AruocaOD0zk8KkHJ7FnNMdoXwVdwqmFFg6NpJmS6JJh4/hC\nBzXDxXsO9gMAPnt8EQMZFXeMpvGPpwlFtmW6+N7DfSjFppMJEd9YqGGm5kCj53V4kNCvduVSeGiK\nfO7Z6Tq29SSxWDcZTSGpCDgy4GO20e1V603IkHgezy212HWbLHWgKyLef/PQNYO4r0wuY7lDNmvv\nPzyEP3luDu850IcXy2RM3tqfhS4LmG93Daonqyaato/9fToG6KZu2bTx3HwHtwwnNwwEXi18u+an\nbRmdbYR1hdAx3CDEMjWvLyRJj8F8zUJilaqfJPEoGw625cg1cmnfWNv2mTJiQhbwXKmBd+bJ86hK\nREAin5RZ8BdGhHYZ94ACQFYSiMmxG7BNeT4pQxI4RBGQoIuFJguwvRDphIQZSrMWeG6N+uBqTFCV\nQssNYDo+elMKW4DiPiSOI71zADCSTbDgLg46swkJiy0LaU0CQDbz0/UObhnKYbpqYKFDkkwCx0ER\nBDxHjb1v7c/iQqODlxY6OECTJbt70xhJJfArXzqFP/qBmwEQj8OEJCDqAMD1j7/RVAJjPTqeu1QH\nQFRZvTBkZt8DGRWLTQsDSXWNd1JCFiCLPL4+S9aN140W0TBcLBkWttPg5kAxA4lvrTFNvn0oi789\nXcbBYgYvLpO5+U+fm8fvv3Mv/vr4AgBgRzaJSy0TXz1Xw4dvJyax+QSPR6crEDmeKXo6QYBLLQ+3\njeYZzS2ryzBsH7okMkEYeCEUiUfNJGbyAFkft+eSLFAqpGRcqHTQoyuI07uZhIQwAnb1pFgi9On5\nKnZkkuA5jr1vMKWRcaYIrLUgq8tsrYnHV7ntIEQEeRNEIIe2KgBAb1rBqYUWOHBsLhLod7QtD/3J\nrvrqSttFWpaQo4IwPWkFl1ZMvEJey5vCt2tueqUCuJhqth6/9bUL+E/fNfGKfMdrFas32189Tdov\n3rSPrPN/+I0pAMCHXjeOC6UO/urLL60J4t66fwC5W3+CURI3E1xdDZsJ4IArK3g+dYHsS++cKCAq\nTa35bTdvz62hXALA771zPxMxeXGWPMc/8bkX8OxH34CzS13qZNP08MAnH8G53+n2zn36iYv46bt3\nrDneRjTOqyFezySBuyEFxzh4i5Nj67Fa1fNKgiSvFlb7Cb5SvoGbyQv8OYD/CuAvVr32MIBfiqLI\n5zjuUwB+CcAv0L9djKLo8Ms9MZ7j2E3w7BCG52OqZmNnkQxojiNGxD3Jbp8Ix3HIqgJ4nmMb09Gs\ngudnm9BViQV7QRghn1Tw7CWSubttMI+CLqLUdNBH5db7Exrm22RDFme8m7YHLySN6Tkllt2OWL9J\nhjYxpmURth+g1PbZ8UYzKh5arm34W+NFtqDJsFMhZhsuMiPkcxcbBi7VbQxkFNw+RIKWhy9WIfAc\nSoaFKTogx4oadhcTqFoullsOfS2Bhu0jq4q4nSpNTRRUPDPfYQFhVhOQVgWklGFMlsj1GEzLSCkC\n7j46jDoNsgSOY8bON4IgJIqjB6kamS7zSMsSHpsmG5hyxwPPEYGMuJLoBxGenm/g0oqB7VSxsm67\neNcbxrHYsFExaF9ROomRfAJNy2dG3rt7VOwfL+DsfAMdGkD9t+8/gvd95jg+9/4jAIAvvLCIjCZj\nz3iefU4Viti7LY+Vlo0+aifQo4tIaxKqhs+C64tVC5OlNkYKOusvIRMC6e+LN+SaLBDRE3odG1aA\nIwNJnF4yEEXk8auaHnSZx3TLYIpze4fSKOoSluom6yNMSDwSooCLVRsHB8hkYPlEOa7UcjFE709/\nVkPb8tB2r50qqnQ8mHQzeGaliYIuIa8pWGiSif8NowL++UIFe3oSrBrQSZOqSq+mMDuLmbqNgi6i\nTK/1txB/jm/D/MRxHFs0XT+E4QR4odzCO/eSwCsII3RsIjay+n26IiBChKoR963yeHJuBdtSOvro\n/XP8EINpBccXyQKqCjzGcjpOlJo4TJURU5rIFCjjgL5jk75F2wtRTJHNq+EErAcv3kQnVWIHYNg+\nemhfaW9awYVyB/sGL+9riJULU1S4otJy2LlWWg5qJjFyPjJC+kXLdP6ZrZmoWGQO3ZVPYyijwfZC\nlGjANpFLom350AQBd42S3qVsQkKl7eJQD6mq53QZSVXErrtSeHiaBEpD6QRSsoiffNMO1psMEJbB\n6mb364EfRjhXamNXL1UEpf0kl6j6b91wIfE8vIAIqACkv/WrFysYyynYk0+z6/Gpt+/HfNvEEv2d\ne3vT2JElx42NvIuqgrftLuJrsyv40rMkaPv0u2/CXx9fwHtpQunr5+soagq+Z2+R9X0f7s/i7tEi\nTq00sZMes6gpML0ADWrqDgBLLRsLHRP7i90eR9MLIEc8Go6Lokqe5YQsgOfAgu2qQZgby20bXkjG\nV2wZMdswMZYj4+NwXxaqJOBis8MYC5LAg+c5TFW7Qhim42MwpWGhZbHP9qYULLYsrFzBXH41ljo2\nhjgyDx+/1EBBl5HhJFa5PTCQwX97agbv2tOHXmqhkfACmA6xMTi1HCs/c0RkpWUBuHGp++vEn+Pb\nMDdthN985Dx+8f6d137jKqiygL8/uXCZ8fF7DgysUtwl88BK27mqNPxrEbEIyq6ByxPrqzfbcfAW\n40Ov61bZJ/qTmPl/3o2P/fNZAMCvv4UEc//y+V97xc/3evHhL5zEjx8dwZ0TBfZa/bFfR63jwqaB\nkioL7PfHvadDeY2JmPzuR/+AfO6ZP8DZJYOpydo+Se488fEHmPDNeK+On757B45N1XF0vGtVsVEA\n982LtQ0rldcb8G2EuPoWs5/WY3XI9nICuDDsWuhcCf/uj57GP3zwjg3/FrP50i8z6XLNMDeKoscB\n1Na99lAURfEO8WkAwy/rLLawhS1s4QawNT9tYQtbeC1ia27awha28GpjUz1xtNT/jzElYN3fvgzg\nr6Mo+ix93ykAkwBaAD4WRdETVzjmjwP4cQAYHR29ZXZ2ds3fj003WQWs3HEwnNVguQHLvB4YzGC5\naWOxZaMvSTJAphvA9H0oggCRRtg8x6Fs2ejRVGYeXkgoqJrdLODh4SwmSx0M5TVcopUtTRYQRaS3\nI6ZuZDQiq7zSdmDSvqOUAeskAAAgAElEQVSJniRcP0TL6va/JRQRS00izZyndA5F4lExHDyw73Jl\nx6cvkIx7QNW3ViwH49SouU6rTX4UIkGVIk9UGthfyCCtxZlF0sORliX4YQSbqpat2A5knlS8qjb5\nvb2aCi8MmbyzKgnoOD4qlo2hJMk+xT0tPAdG2el4xA8orUgbKthdC8dnW2hYHjsPTRSgigLr3+I5\nDrt7U3h2vsYMWvf2Ee+fMytNdr6jGR0PT5UxkFKwkyqqfeVCGXt7EggiUiEDAMMN8V3bc0jLEj7z\nAsl0f+jWUcy1TJZhfvfhQfy/x+aQkkVm1tuXUJkKapWOtR1ZHXNtE6oosMz26ZUmipqCxY6Fu7aR\ne3qy1IAXRthbTGOWGkE3XQ839WXZfQwRwfYDnKi0cQv1/ErKIhqOi6wio0y/u+l6SEkSaraDFO1p\nWWjb+OqpCj74um0oUmP2judhNKfj4kqH9R+5QYiMImE0f+2euIfPVFhGfF8xg+WOjUNDGTw2Raoe\nE9kkCikF1bYDg2bv6o6DuuPhUt3BDxwme5CFOlFRTcoi7tnVzbB9K/pOvh3z04uX2sxioWX76EsT\nhca456k/S1Rr25aPQdpHWWkRn8SsLrPKR9yfOpBVWc8hqdh1lSN5joMoELXAmPpS6TgYK+o4u0zo\nZQCwrz+FMAKeurSC/gT5zvEeHXXDw4vLddw9RsZpQhZwiT4n8fFUiSfWHEOXVylOL5KxbFOT5lrH\nZVQQ2wvQkyYUzrhit9i2sLMnhYbhrqH4uH4InuOQoIyFlY6LlCIijCKWNdWpxULcP1VIylis27D8\nAEU6z0dRhLQmgee6Vb+4J07gORzdfv3z07mSidmaAYkna44dBNAlkVXZJZHHiuFAFQQmG217IXiO\nVPHi69GwXZyptrEnn0J/ilSGfvfJabzvUD8SoojjZTLXP3K2ik+8aTeyuowP/H/PAwB+4p4xDKe6\nfnuv35nDg6cq6NEVZt5cs10MpFQ4XoiSSe5hXlXgBiHSSjeje6HRxs0DOSw0LQzQ/rEwjNCySEWW\nmXsHEQZyKqvGe0GIhZaFqu0ypc4witCwPJhewMzaG5YHXRZRMRykKIX9TK2Fj/7JM/j7X3wjGk7c\nMw4UE4SaGTNreI6DIvGwvfCa6rnfvNhgFGKe56BKpH/4BPVWTcsSBrMqah2XKX9GiNByfXzpxWX8\n2ptJZcQPI3h+CNMNcNcuUiX4Tp2bXgt9YlfC5FL7ssrX0xeruGNH4Qqf6OK12hN3PbhRdcpXCp85\nNoP3Hx1j/737Z7/M6JB/9swMAGLkHfvA5d746wCAmb//hTUVMlW8+nnFHrsbUXvPLbaxe/DG/WRP\nz7ewb/j6FIgfOlPCe3/o1xnFdTO40Z641XT79fD8EJOlDt7wi3+Hymfff8XvfEV74q4GjuM+CsAH\n8Jf0pSUAo1EUVTmOuwXAlziO2x9F0WXGIlEU/TGAPwZIc+76vz+1UMPeAi3zdkzI1AvoSdqzIHI8\nFgwTZysmk4LPagJSioBS28NAmgyevYUUlg0Hf/dSmfXJuUGI33tsCu84Qsrk2ws6Xqw0kE1IeIhS\ndvb16mg6Hm7py7GbUWo6OFNtYm8hg2qHBHvZNvmelCbhmXlCQSuoCp5dbEIReZygRspHR9OoWf6G\nQdzJFbIgnS1bGMgQuei4f+X0ShsPnV7BgeE0BtMkIHx2toWLVRtv29mL55bIpfWCCBMFFVXLw9cv\nko2CH5BgbN9AEis0iCi3VjC11MLbbyHN8+eWTWzLqzg208ChEbL5san5t8BzzBR8W1aGHwF7CzqA\n698kPTlXRcXwkKd+fmlVQMMKWC+GF0Y4NJTFM3NtLFDfjp+7R8VM08BffHMB22g/zA8eUlBqeZhr\nuHhhkVCM7hzN4NyKgZrZFa/Z1avi8ydK6Esr2EkDGdMN8PkTJWSocIrlB/iPR0dw/x88hfvpfQmj\nCL//Lxdx2+6uwen5io2m7WOq1MJP3EsMMVdMD6fKJs4uttFLaYZfPrOCoayCvCrjeUrV+OZUDeW9\nLqO8CRwHww3QsHx8fZ6M5Sgi9NIDPSkcWyT3c6nlwrCJ2MVt28m4Nb0Ad+0s4MHzVbxnP+nnPFVp\no2TYWDY8NnnM1hxMFMk5XSuIe/B8lW2E+49q+IdzK6jaDv51ilpXHNRwdnYFqsCjusq64lTJwpsm\nCswb5WszVTRsH7eNpABcTpP4duDVnJ/qlositcjIJSSIAo9sQsJcg8wLM1UDWU1GGEWYLnf7zjIJ\nCabjMwn64WwCKVXEueU2JugYX2k7+OLpJbzvEKExGbYPSSRUtZguZwfE0Duvyl3J+zDCXM3C4f4c\nC+h5jmx879rWw5JRfhih5XjoTSp46MIyAOCukcIVe+LiZ/Rio4P9cgaZhMQCNj+M8MRMBfsKGUYL\nl3ieBnpdq5c4MeR4IU6WydjyghCDkYaMIrEg6PhyHQtNF2/eQZ7HZ+dq0CURZ6sdHIrIvBNGEaNN\nlildcyybhOeHN0zFqXVIH9sdVJLedgM0bQ9ZSovmOA4pVUTdcHGxTmnnugYnjDBZb2MPFVXKKBJu\n7SdBwpkVMqw+dv8ELDdAFJHrDABH+3MotW0IPIdfeeNuet04XGqZLEHz4KkK3ry/B7/9r1N4F6Xp\nchzwL9MV3DfWg23UbH6y1sZYWseZahM7MmTN3J1PI4qAtutB6pC5JykTWngYdXt0LjRacIIQw7mu\nUIjE8zg6mINH7/Fc00ReVVDQuh6IihtgrmWg7foYyZLfe7OUw29+8HbUHRfbaRJyvmHhhXIdd2/r\nYWOhY/tQRB6V1rXplCuWw7z/iikZLcvHU/NVZGnA2ptK4oWlBraldUS0O29HPom5hoVPfs9eNk4t\nN0AQRkhrr5wB9cvBqzk3vdr48b9+EX/83ptu6LMbURevFMD9wZMXAQA/edeODf++GhvJ7gOkH2w1\nnfBaiOfqpunh8DpRjMcnK/jCqWUmAhJT//7k6Wn82B3bLztWnKhb33N2I3hmqsbM16+GUoPMh/1Z\nsv4/fGYZb9zbtfyJA7jYG211P9sHbiN/+8AqI+/YRgBYS3W0fRJw/OpXJ/HL9+9i7/m/HzqHjz+w\nmwVvsQn36vsQB3DXok5eyV5gMwFcy/IwXTZw0zZyDx/Y249//Kv/67L3PXJ2Gffv6bvs9RvFQ2dK\nOEhbHgayKus/jFsfJJHH/uH0hgHcjeKGRxfHcf8bSNPu/RHdkURR5ABw6P9/juO4iwB2ATh2peNc\nCb26zLKidctHb8JHGEVI0SxoRpOwZHKwvYgFGnt6VTw3Tx7CkSyZ+MumjbmmAzcIWSAznk7i4GgW\ni61uP4XAcagbLus/2p7W8dgl0nsWLwTzbRN1mm0v056WiRwHzw/RND20aaXCCy30JCVUOh529ZLF\njOeAmrFx6D5VIw+eJvM4PtfC/oEky/KrIo/dAylYXgjDDen7RKgij5rtst6/KIrQmxIh8hwyCfLb\ns5qIFcNDLiEwlS6B57B7JAvL7fKFLS/EE4+fw9A7j9DrqGGq5qDctNmx5psuirrENvLXi5mag/vG\nc/iHM0S4Y7yowgsiTC6T+5VOkOb0huVhgE7GssijR1MxkEugQa93XEXr2D7ydIM137IxW3MwW+lg\njPbOybQh9qX5Jt6wh2zMypaNk5Mr2EMnw5Scw/1/8BQe+ck78aOffwkAMJSWkUzIWG7arKpy10QW\nJxZ9ZHWFVTnPli0EYYThQmKNifvzMw3c1J9C3I/bk1YRRuT3A0SQoj9FNsFGSI41klWQlAU4Qcgq\ngo4XoJCSsatPx3yza0z53QeK+Eezwr5TlwVkFRkPn6t1DW4FDk9eqGNP4dq9HyHAKj4ONQ/NKjL2\n9pGqbNV2SIVNUpgv38llAwMZGZfaJsY4Mr5rlg/TDdByXhup0Vd7fhrIaGwjXO+4EHii8penCYKE\nIiKKIthuwIQdBtIqpqod5FYFXnXDZRO8SeePpCribbv6WDZTEnioEo8XS20cotWR3owK0/GhSN2q\nSqlho+m66IHClAWzCWL23TQdxr233AB9KRVRFOF1w2RhlUWeNZSvR1whzKsyTpWbONSfBceR79QV\nEUcH8+jYPvMty2syFIlHw/SY4XwUkUqKIvIYS9G+WFWE64fIJiQWACYlEW/ekWEBpyII8MMI/+Vv\nz+DTP3wLAGBPbwqLDRsXm21Woa+aDjKKxDZO14vZtoF7t/fiQoUEaAmRXNczZbKHHtA1cBxJcO0q\ndDeimixAEXhWvR8QNYgCD8P1mUpjte3C8HyUTBs7c+SzqsTDCwWcrrQwmiLP2kzLwFfP1fA9e8l8\nNZYharo/d+84Y2v4YYSd+QSqpsOu0d5iGkEYYSydhEPnlIW6iYlcCruKaSy3yLlJIo/jpTomsinm\nfzeRJUySWE3X8UNI1Gc1oHNtf1JDmlZg4+RAGAGjaR1ZXUK56bBze+NEHxZqFhuTOZrImKy0GcMi\nn5BxbL7OVDOvBlng16ie8hywLZ3AKO2vaxguOh4JCseyXfbKSFbDUtNmCsamRxJ84gbZ8W81Xu25\n6dXGRgHchVIHE/1XX29WC/tsBpsJ3mIM5jQs1CwM5dcGctcTwAHA9t4re51tL+hrVBzjAOTnf/J3\n8CPfJL1jcY/UY+cqGC9QQbKXGcRNlY0NA7h43Vh9TePgLcYb9/axKv5qH7n1/YtTZQPj63776us5\nu2LigU8+gic+/gAA0kP9q1+dxCfetGtNReojd631QY5F2Vbfh1jRcn0A9+JsgwVdAG7YHw4gfWar\njwV0zc9XY6MAbjP+dVfCA3vX9k9eqpMg7kYEWjaLGxpdHMe9GcDPA7g3iiJz1es9AGpRFAUcx40D\n2Alg6ka+wwlCWJRqN5rRUDZtHO7LsYXL9UP0J1T0p7rZPJHnMZpT8Pi5Km6nKpA8x+H8soG+jMqU\nxhYME8tNB9tplSKMyCY0n+zeuBXLge0RCfUlKnDSn1ChCDxkkWcbuNmmgaGkBg5gRtO7Cgm0bBtZ\nTWSb+YTEM0W39RjPU1UtL4QqJnGp7mBPkRwrp0poWG0kla5htOn4sDwRfboKhVJUdFlk2clSg2RZ\nVElHEIQ4W7bZ+7blNZTaLso0+GuZLrYXNLz3HUfYxnK24SKrCUhICRb4phSymQo2Qb/dCDuKKk6W\nO0jSIHwko6Bh+8xg1A0ihBG5h/EGwPVDSDwHPwiZtH/NcXFhuY2+rMYa6i/VHfSkJLRtBXMrBr0e\nAgzHx1BeZwqhh3vTuOfIEBMx8cII9+/rwY9+/iX86feRyfkzz15CSpNQ7zgYpLLhisBjuWFB4HlU\n6eZ4JKtgqmqhbngs2WC5AXK6grrdvb6ZBKF9dWjQ7PoBbh9JY6HpIkuzwl4QQRZ4rNgOXqBqUGO9\nKcxWDHzh+DxUmpl/z33jeHS6ClXicIHSNcMowo5CEjt6Enh2us6uWyYhMxuJq8H1Q+wdIJMlz5HN\ntioILJgsagr6EirO1Fo4sxyPKx4CDyiChIbTrc6NZBW8jD7hVwzfkvlplU3AQE7F2VIbd4znGRXu\n0oqJUSq9vJoONpDS8OxSDfdSamPL8nF6pYU9hTSreM01TBiej+FUl97csnzsKaRZpbnSInYWksDj\nQo0EHvv7MiikFEgCB9MhB5uumKz6EAc4hZSMpklkyGWxuxFo0arWevRTwYiW5aE/pWKpaTGaYU9a\nYRWV+LU4GO1NK6xiRujZHFRZwAlKKdxbIMHHdNXAAKXp7cgmIfAcZlbRkcfTSfzPD97BEjjlloO0\nKmKfkMFUi/z2lCytMUy/XuzMpXBiqcHEe/JJUvWJLQaArsrc6vkppUnwgogFELMNIuayJ5eGKpDr\nMdMysLuQAgcO52okKHSCEAVVxng2iQtUnbgvoeLDt48yERPPD/GuvQN4+kKDSfE/PllDTlEw1epg\nL63+CTyH6bqBrCJjiRpf78wm4QchmmbIgknbDdCvq1gyLDbW0poEL/DQoSJIVdvFof4M/CBi82vb\n9iEJPObaJi426VgrZnBqpYk/+rtZKHTd+NTb9uEr55Zw20AeZ1eoUX1Sw6GhDBqmh29SpkrVdiDx\nPHLatTdKphdg/wCpwDZMD2EEJESR3YN8UsZtah6LLQtPz5MK7+1DGeg+oevHiqm6JKKQlG84yH+l\n8K2Ym14Onjy/grt2dje702XjssCmbV1uYXClAM6g11tXxVd1IwvgsgDuRhCrMa4PAICu2Emc7Dq9\n0MIt23MbUvTesLsHp+cvK6LeEMZ79Q0D4M1ez9XB26Nny7hvT+8a8+nhvIbxXp3Za1UMG3dsL2Ao\nr60x8j73O29nIia9aQW/fP8uVpEDumIn68dQrHJ6il6P/VeopN20LYuG4TJLqPWoth0UriCeU27a\nLNm4dyiN0/MtvP77fwPQyHfVH/plJtITr1c9q8ztn5kiravbCoQZsxmYjo+EcvX3xoHjh79wEgDw\n3999cFPHvh5sxmLgrwC8AUCR47h5AB8HUVRSADxMs/+xHO49AD7BcZwHkuT/UBRFG0sybmELW9jC\ny8TW/LSFLWzhtYituWkLW9jCq41rBnFRFH3/Bi//jyu894sAvvhyTwoAJjJJVhpebjo4NJCF64es\nX6CYklHrAK8byqNGKX5ZRcJYOsKBniRsWmreU0whd1TGbMvECM1s7yjqyCsK66fgOeDm/jR4jsN9\n20jkrMoCehMqJIHHSJZ6OvkhRtQEGpaH3XmSedJFkXo2SThAqXx9CRVDyQTKpo1p2iOzpzcNbdfG\nPSd9tLdGE0m1a0+xm4X3gxD3jmfQdgP00/eN35RAQVWQ1kS8bpRkKfOKAkXkockCvvcw+e0ZWYLh\n+xhPJxndJ0IEboDDXJtUVb57IkdoSH0+2lQ+3w0CDOoJ8BwY5WWmbSAl/f/svXe0ZdldHvidnG4O\nL6d6Va9CV+xUnbsVUJYwSIIBLCGMMcYWjFkMDDDYgzRgMAsWHhhEsOXBAizGQpJBwijTCt0tlbq6\nK3V1Vb0KL8f7bj45zh97n/1CvYrdEoVUv7V6re7b7557zt777P0L3+/7REaycavWZygYynLQ+skY\n1BwP+8s57C+TTElBkyDyHN4wUWRQyKIh4+WVDl6/u8jGI04SvPVgFWVVZnMs8hx6NRUHew2W/c7I\nIp5fbEMWOEzQ/oyyLuONu4pQBTrHooA4STCYk/GR52cBAO97cAQLHR/jJQ3nV2nfoyrhhx8YgB/F\n2EepxNccDwd7MpjrOhij1x8ta3jzzip6cwoUocHuLSdL2EshWJLAQRUEvGYHj8tNktV63Y4qvjJT\nw5MjFZQeoUQ4goC5ro6m6eGX3kAw5w3PQ1VToAgCTlDq7LdM9KJlB+jNSvjfqXbPy/UOxnLGJprk\na9mbJ8oMItqf0/DEeB5lQ8YYrb6UDVK12VfKsT7NsayBjh+iostMS08XRSzbDusN+nbZP9T+FEQJ\nevNkrhqmjz29WSw23fW+MIHHcsuFJgusGqXGAkSBw9GBEiZXSUVjpKjjoJbHYsdBhcJVd5Qz8III\nl2mFTRUEVA0Fy6bLqhJhnKCUkTG9RiowAKnOlTIyjs01sCNH9iIrCLFoORg0NDh0ni0vgiIJaJg+\nji+T6u3rdlSvqauT9kwS4pAEmiiwfqmOEyKKEyxYDqv4TJSzUGWiEZdCfFNRelXi2T5MMskxxsoG\nkxXxKLwyJak43FMAz5HfXqR7lhkE2KFkoMkCHhwkMKOLNRN5RcJtAgWw0LURxgmDPr2w2MSuQoZl\nhfvyCr5ysYa+jMYy4FGc4EuXV8i7QTOyC6aDgYwKWeTRTsmMNhBklQ2S4W/YPma7NpKOjbJK5t0N\nY5R0Hkf6yN9cqVvgKHHKVyfJfvLk7hI+eWoJB8p5nKqRqkGPrmAkTwS6d/AG/U2gaEhYaDsM4nt6\ntYWndvTAC2MsUrSGLPIwgwC9VGOtpMtMXP74Ilkbrxmv4uNnFvDESIVV5xSJR0GWYdsB/vAHCbxu\nteuh3yAi5J+5QKpu/+Gte/Hc1BriJMFrdxLo0uyajWpOuSloY49OSEsA0g8YUTRIjfbilg0Zlxtk\n7jUqITSQI8LiksAxNIIk8JhqWEys/Nth/1B70yuxjRUUYHt4YVaTGPGQLF6/GmRsqWpsJTZZ7XgM\njbPVPvoiIWz5kftuXZd2oy02HVRpBUe6wf1uV4Hbak9Tbd637u+/6v+Z7jr51K2Sb1zL/DCG7YVs\nL9oO+ridfe7l5U3yCB8/NY/vO0C4EObq9lV/v5f2qu1FFmdm2xjvNRgcMrXtfjctbqsisNz28fhE\nBV84R3qtX7u7yrQGr1WBA9Z7/q5VhQOwqQp3Zpb4PwdH8vjdr1zCzz21i0mMAHTsXRPNr/0H9tnb\n7iHz9Zp//yUAwNnfeiv8MEYQxjfVb7jVblSFAwDLC2EoIvpfgTzXjezO6PLdxs7WO1iwyEHTcgP0\nOgq8KMaZFeLYvGtfPy63TDx9ucX0sWpWAInn0LB8DFOIYpIk+OSZVQgch8PDxImJkwRfnWky6Ndr\nx3mcXOkgr8j40DfIxvHUrjwmay6+f28va+ae79g4vWLitaNlvFQjpefhnIoeTUXT9HGCklmUDQeS\nQMgfUthly1nDfNvDD993NaPwBaqrUzNDNKjD9/pdZJP80qUWPD9CT05hfXLPXm5hsKjhbRNVfP4S\nOWi7ToD7hrPQJR7H58gYhXGMetfDgaEcK2UfHM7hv/z1WRw6RF7mgZKOoi7hU1+bxjseH2P39OnV\nGjpOgLccIgfvUtuHFyW4p1fbxD54s3ZyuYs4WdecWrMCJEmTja3Ac/jXD43g46dWIFBn8l88KMEO\nQ/zps3MMa/7Lb5jAc1c6aNs+DmzYFD7XrGN61cTEAPnsYL+Bl5dM+GHMBMUfG+bwW5+dxD4q2P2m\n3SX8/hcvI6PLDB6y0PHxf7x+F970oW9gqEI2rKm6g68dn8fuXWW8Zu96T+CZxS56cir2lYhDUbcC\nfOzsMh4fy+HMMiW4WLPw4FgeJ+fJ2ujLqdhRVjBV95hzXLNCGDKPiw0Tz82QzWm2bkOVBGiKiP96\nfB4AcUTedagHxxba6MuS+/3w8Tk8OJTF6UUbz1EyksWmjeGygR8TBYyUrr95HFto4yW6Nn7mcRmf\nP1+HyHOYo6LV3SCEH8U4UM6zfrfnrRammx7uGzBYAPH8YhtdL4YdxHh0orj9j30H2UZihowqwnRD\nIvidsipSnbbllssOl4WmgwztlavQXlMviLBmeygoMsPhW14IgecYcYWuCCQ4MYGXKCsfQPRuNFFg\nv9W2A5yvdXBvfxFusN4H0Z9TYXkRstL6dq9KPGwPuK+HOC2OH11TfDTVnFtpE3ZNgV/X8Dyx0sSB\nch77KuvvoumS/mWeW9fkmWs4MGhgl1fJ2g2jGHYQQXJDBlkfLmj44Bcmcf8ouV4UJ3CiGP/p+Vn8\n7KOEPEAUOJxda2Ou7TFdvrImo+MHKGm3d1gWFBltL2BBZ5wkOLvWxiGeag4FEQayGjpuCDWiMEM/\nxJGeAi42u7jUIu/3g/0leEGEVcvDMGX79eMYtY6HJdvF3gpxlAxZxJ5iFgnAGHo5LsbfT9XwBNXM\n68+q+OJUDRMlHUWFPNcnTy3hnYf78clTSzg6QPaxkyst/N3zc3jv4QEGdzzSW0DXCbGrmmEMw0/u\n6IHlheg6IfrpmrS8CEVVxgJlOa5oCkSBgyoJGDQIPO3CShdPjVbghzHTv1xuuSiqMn757XswRwNC\nSeCwp5oDz3N4z2EyL89NrUHkyBpMSRfcKILphhTSeX0InCrxjMynr6DC8SPMmy7uHyR7zFTdghtF\neHSgzFhaBZ5DGBP9Vn5DEmFUNtj7eddemd0oeNvOOk5wFbHJtQI44JUHb1+drOHJ3dWbFsu+GRv4\n8Y/iJ36Q9OamQdzoT/0VZv74BwCQs+DCYhf/9eTCJgHwV2JkHa/v3V4QXVNnbaO96Z4+TC510aQ+\n5bsPr/ueWxO8tY63CV54cORq8rqtQt4f/PwF/Ozj46y3bbntoy8vY6HlMzKVF6ea+MmPHMfxD7wB\nL9B2j/t3XO0f3GoQtfH+fu6p7YXnr3z5dzf9d5p8O/tbbwVA4OqyyN/WWr6Wbe3LrHU8GFUR/+4N\nu6/zrVdmd2wQJ/BAVlqv+ogcD1kSsK+6XhVLG6VT0o437CzhctNC0w6g0onJSBIqWQUztXWBa00U\noEk8Oi75nhtE4DgOOU3EvZTyWOA4cDQDnBI/VDUFe6sxRJ7DAM0KqIKAGAkUaZ16uj+jwgkJ5fYh\nKsq8agaIou0PEC8kgc2+Xg3fmCZCygMG+V5fjjAgZhUdDnXMspqEpbaLrCKiopMxaFqEvEUROTi0\nv8ENIoxUDORVAT1UuNqQBbz9dbuh0LHwwxjjZRU/8vqdmKOHrCIKeHBHAXYQM7Hv/ryME3MdnF2+\nvVR3SROR10R8+SLJHg8VVQg8ML1GHADHj6ArInK6hAINqHRJRFlVMFgxWHMuoZRW0J9XsLO8nnlZ\naLpQaR8cAOQUATldxnzdQkEj89KbUbF3pIi1znrvz9E9PVhpu2iaxCEfL2l404e+gc+9/2H85F8R\nspPHx/M4N92A5QR4aIA4dX95ZgmKKJAgkcomXFnpYldfDuP5DL4+22W/oUn8pgqBJJBEQ+p8e2GM\nPVUN/bqKheYyADKfT+yuYKiksnk3vQgyL8AJYlatPDqURUVT0DA9KNQJT5k8m96N2d+iOGHkNX4U\nI6NKGDJ0fPIUyaT9sweHMNOxsWK7uIdWTWc6FmabHsbzGSbmLPE8WpaL2TuAOODbYX4Ys6yuJPBw\n/ACSyDMnt2kRZsOWt84OeHgkj+WWi5WOx/YnTRaQkSRcanXZeuA40nfVdgP2W6osoNdQGT3/y6sd\nuLQvr+Oss6CNwkAUJwzX33ECVpVILasSWv+m62NnhayVeteHHWzfL5Rev5pTMNskNPwDdD8pWhIW\nTQc7Sxm00oqjxNLjgTwAACAASURBVONcrYP7Bousd6nmuPAiwuK5bJI1Y4cR9pSziJOE9fMmCfCL\nr9nJgjovilA2FPzK6yZwlrJa5mUJh3sLmChEjLSjoEn45lITQdTF/WO3ngGXeR79GRWTlFEyK0kQ\nZA5zHZveR4zDfQW4AZF/AMi7oysi+nQNbd9nzy7yHHKaBFFYZ+acadoI4xjL9LlKmgzwHGbaFvpp\nsFQ2ZIgcj7OUrXh3IYvXjVVRtz3W+3egnGeB3PM0aXN0sITZlofJVhePUKKaY/N1TBSyaNsBVuh4\nt70Aw1kdA0UVS3SvN4MQOVlCk/a2qoIAQxax3HJZ5X2qY2G4oMNQgAurZF+b7zp4cqyKvC4xEp2O\nHyBJErSsgPUv7uvJoWkFuNw0WcWxYiiwvBBrjod7cf256rghIxtL+9lGcwZjVX14sIyptoX5hoMd\ntGrghzHsIMRoJYeXKduvLPJYMV2sOh6OjNw+vfl3o20nC3A9uxbr4CsVNb5Vu2+kyCohG2217W6q\n2mzHbLk1qGF/+//+yFWfzfzxD2CeCmUPlTTsGcjiNwc2B3B/8vUptJwQv/rGWxNcT00UeLb+9w2S\nd8ajfm+t6zPOgK22uz/LkCDXM8sLUcXm5906j6mQNwA8MF7Er76RsOo+c5GQ1T0+UcFCy8dgQWbV\nuft2FPHQwT4WUAPbM4ZunZONdm6hs+m5N9p2PZv/8+wS3ra//4bkJOn5vd38O34EVdw+qXk9Gyxp\n+NjJOQDADx4ZxuRa96pq5rXspbn2bcnj3LFBnBsmcCOyElYsD7uLEuZNm5GTNF0fQRyjJyuhTlkf\na46LS3UPeV1ipBGqyIPngANDeVxeIwfX0f4idJlnJB2iwCOrEEKNBnVY9lezaFKokEeDr6brQ+AI\n9b5JHR6B4yBH5LtLlECjqouIkgSaxOMlWpEZzMuspL/VUqbByRqp3DUtD3NUwmBnWYXjx2g4IUYp\nbO+FmQ7KWaJR1qZZ1rwmQxZ4mH7MGm9HKzoaVgA3kFjQVtJEnHR8DChk0bb9CAVVxGxz3eHf16th\nxQzg+DFcGjxFcYKxso7dlduDowRxgqJCAjAAkAUOZUOE5ZHrr5k+uk6AAtV/AoiukchzUEQBZaoR\nNdu1kVdFnFu2MEQZSF9aslEyZCw1HVZNcMIYXSfASMVgEhSKyEMSePRSBzTVgYvjhJGYnF+1MVQx\n8JN/9RL+0w8QspP//I0ZHNxZxokLNTbvE1UN55ZtSALHtABFnhCgLFkO6jQojOMEkzWXbShRkkDg\nOBiqxOBEh/oNLHZ8ZCSXwQkW1iycWzKx1LDRT+9NEDgs24T5La2KNZ0QhiRCEnhWTWyYHlRJYFno\n65ki8Gy8lywHjh9ClXjspWQnNcdDQZXwwmIX+3vJunpx0UJZF3G5baKPEkGsmAHGyhpjj/1OtzBO\nkNB3o2n50GQBs02bBUVLXRcZVURfRkWXzlXD9DHfclA1FHaAyCIP3g1x/0CJOdbjPQYcP0ImIHuY\nJPLQZQF1P2JVlV2lDFRJQNPy2eHuBREMRYQs8oxcxA9jcBwHngPWbLLeFUmAyHPIShIur5HgoGqo\nqFxj7lIWtNmmBZkXUHPWYaMTFCZsuiELYAmsTsNi02XBY5+uIUZCHGxaeZooZVggmu5POV3C9JrF\nYHtWEGJMNdBxAlbVm+ghGnRBFDNiHQA4WM2xKt+tWpQkqBgKy9Y2bR95VWLoh5YbYK1LzpYUXRHQ\n/UmXBBQ0sjdfqps40JfHXNNGiSZHXlhu4mAlD6sdIkOroX5I9tY9lSxqdK8wFFIxnSiQMV22HYzm\nDcQJGInJqVoLRwdKeP5KGw9Svc5zixbeuqsHX5iqsb1/fyWPKE4gCTyqFELoRzFmuzY4jmPw+qwk\n4XyjwyCuKYOjLAusanW4t4CG6SOMEwZHvFA3caHWwWTDxih1vgSOw3KHMEK2qU7cWteHF0XQRIEl\n51K9QEW48V6hSQIb74W2g4wkIq9LeLCfZO6Xug76DRXPzNVxtJ9k+JctF2MFA9M1ixHTNGwfoyUD\neueOdXnuWLuVAA7Abct8vBq21HLRT9twrsUKuTVY2OjAr3WvJr24GZO3gaJvhJz+y0euliC4Vdv6\nPGnS9loBXGrpfGwX0KbB1XaaZtvN4wPj5B3bSG6TQnBTOYOtZCcfetdB/NvPnGdB3HaMoT15lVWx\nNo7bVsbKrbYd3Pdi3YYfxvidr1zCe6mOra6IjJE3PWfTdbJdpfZacjs3Yz94ZJj9+xv39eFPvj7F\n5t/xo2te+8DwrQdwAPCtpQu6a3ftrt21u3bX7tpdu2t37a7dtbv2qtodm5bqMxQM0p4CL4pR0mR0\ng5BlRosq0ZF7YqSI83WSTe7TNRzoS8BxBPoIkMrNQ8NZKILAmuXjhMAKdxRIBO76EbKyCI7jsJ9G\n9gVFhibzyKgiqjRy1s316kbaXzKSIw3leV3CwyMkWxrEMSqyhFY+YpUJjgODhWy1FCLap2uYbHUR\nRBr20x6Tc/UORooKljo+7qGkGv6eGMMZHRPVDCYbpGI3kJUxRMlU5mlmZk9Vw6IiQJXWsyyywGG+\nZuHN+0g2xAlV6KIAO4jxDqpPNNN2IXIc9vVqyFKdHUXgUbN97LlN4or+rIKmFzBo4OeOL+AX3r4b\n/aMkGyLzpEo2kJNQodWoclbGyaUWDg9mMEirmLoowpB5PDSWQ4aO5+t3FbFseqhmKsip5LOiKqE3\np2B3VWVrwfJClA0JVYM8086CgYs1F4/vKjBShoIqYaru4PHxPP4z7Y/8Fw+P4uTiy3jnoyMMYqRJ\nPJ7YmceJBYtRq7/tUA/sIMZ4MYPHxslai+IEwzmVZaaDOEG/ocIsR0wAPIX3llQZY7SXc2elH10v\nwlhFxxjtawuiBL26CqmXZ1W8giKjrCl4bGee3dtLyzxKuojB7I2JTY70ZxghwAP9JdhBjN99ZgqP\n0LL+gEHWwIChMWjc/YPAsuljOKMjpgK7B/s1NO0IY4VXr//gTrY4Thh8uuuE0GQBThgx6Ioi8PDC\nGL0FFQ6laY7iBLooELIKWt36xvwaHh2uIKOKLAu82vYQROuwvSsNk/RjeQGj4g+jBGtdD7LIM80f\nywuR00TUTZ9lMzOqiI4TwtBE9PLk79ZMD5WMAieMGCTddMNrCiGnPQMT+SxWOx5GJINV3RZbLnKq\niKmOhSfLJNPKcxxyNIt7cYXA73qyKgSeQ9cJ4FOClSQhUOKG66MbkHHbk+TwJ9+cxb+h/W85WULH\nCXBiucmEuGfqFhquj6GsjuEclQGRBKx23duuAgRxjFNLLbRpZe+Df3Eaf/kzjzNyrcGShosrJmwv\nYlI0RV3Ci0tN7C3nmGbluXqHVOxUicFQ7ykRKYUdeYNlz+uWh44foE9QUaDEI5dqJrwoYuRdJVXB\nZKOLfZUcy5T36ApOrrRwdLCEc4tkXe0bMPCJU0t443iVVWrbvo+JShYvr7YZXHOiksVa10MlK7Pq\nuyYLyCoiu74XkDc6iGJ21nacAAuWg4qqsNaCx4bKqDs+dpd0DGTI9cMogR+TPpOUdEqTBSRegmxG\nY0QAy20XisCjeBNV06wqMtmOPb1ZtKwAb/+dr+BPf+phcn1RgCGJGMiu70+9uoqFjoP+rIoVWuUs\nqhIWWw7bI7/b7HoVgGvZly/U8Jo91W/RHX1rrL9wNVLoWqLR21lKopPuqUmSMOr9fYO5Tf1vqW3t\nT6ubPsoZ+VXttaqbPk7OEyjju9/7a9vKGmy1rc89u2ZfBUlMq2Nbe+R82i92LctqEhPyTu21u6t4\ncaqJ+3YUN5GdzDY8/Ppbru4P3EgCA6xLRGz83ZshmtkKff3ZJ0nf3s8/tWvbZ6jTc3a7tfJKrfjg\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du2t37a7dtbt21+7aXbtrd+0fkd2xcEqRW9fFWLQc9Goqwoj0JACkD8UPY0x3LVb18GOi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\n4yfFtZdfavO9kzVmKvMK8jZRdvnmoWUmB3NsL4prf3CuwlMLbTpexC/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rdOZpuqjj3GoP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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XF_j9kd19Y9",
        "colab_type": "code",
        "outputId": "e37a10bb-77dc-49fc-fd7c-b2a410e0e80b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# these are the positions that were mutated in the library\n",
        "print([parde_seqs[0][n] for n in (58, 59, 60, 63)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['L', 'W', 'D', 'K']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MjyG4CSR2TBm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load dataset\n",
        "lib_a = np.loadtxt(\"Library_fitness_vs_parE3_replicate_A.csv\",np.str,delimiter=\",\")\n",
        "lib_b = np.loadtxt(\"Library_fitness_vs_parE3_replicate_B.csv\",np.str,delimiter=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pPRVZhvx2boM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "starting_seq = parde_msa[\"msa_ori\"][0]\n",
        "\n",
        "# lets parse the data\n",
        "mut_seqs = []\n",
        "mut_scos = []\n",
        "for a, b in zip(lib_a,lib_b):\n",
        "  mu_a, sc_a = a\n",
        "  mu_b, sc_b = b\n",
        "  \n",
        "  muts = [aa2int(aa) for aa in mu_a]\n",
        "  \n",
        "  # starting sequence\n",
        "  new_seq = np.copy(starting_seq)\n",
        "  \n",
        "  # mutate\n",
        "  new_seq[58] = muts[0]\n",
        "  new_seq[59] = muts[1]\n",
        "  new_seq[60] = muts[2]\n",
        "  new_seq[63] = muts[3]\n",
        "    \n",
        "  # save mutated sequence\n",
        "  mut_seqs.append(new_seq)\n",
        "  mut_scos.append([float(sc_a),float(sc_b)])\n",
        "  \n",
        "mut_seqs = np.array(mut_seqs)\n",
        "mut_scos = np.array(mut_scos)\n",
        "fitness = np.mean(mut_scos,-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5dq6-cGk2rAi",
        "colab_type": "code",
        "outputId": "90a3b928-effe-4b07-fb93-36aaaa730137",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# compute score\n",
        "pred_scos = score(parde_mrf,mut_seqs)\n",
        "\n",
        "# substract wild-type\n",
        "pred_scos -= score(parde_mrf,starting_seq)\n",
        "\n",
        "# plot\n",
        "plt.scatter(fitness,pred_scos,s=3)\n",
        "plt.xlabel(\"fitness\")\n",
        "plt.ylabel(\"GREMLIN score\")\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Pe7Z79C/2Q2H5Dg4cs6KcNu86hAXrHh21jbuUNwCUWq0igoaDxyxHbLkktSJ+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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uf8Qbkwfr4ke",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}